{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"default\")\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.arange(100)**2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([[2, 3, 4, 5],\n",
    "                 [6, 7, 8, 9]])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Erster_Eintrag</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zweiter_Eintrag</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a  b  c  d\n",
       "Erster_Eintrag   2  3  4  5\n",
       "Zweiter_Eintrag  6  7  8  9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'], index=['Erster_Eintrag', 'Zweiter_Eintrag'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4, 5],\n",
       "       [6, 7, 8, 9]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Erster_Eintrag     2\n",
       "Zweiter_Eintrag    6\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Komplette Spalte\n",
    "df['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 6])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Erster_Eintrag</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zweiter_Eintrag</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a  b  c\n",
       "Erster_Eintrag   2  3  4\n",
       "Zweiter_Eintrag  6  7  8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Erster_Eintrag</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zweiter_Eintrag</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a  b\n",
       "Erster_Eintrag   2  3\n",
       "Zweiter_Eintrag  6  7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['a', 'b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.983047</td>\n",
       "      <td>3.957817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.128419</td>\n",
       "      <td>1.227702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.618476</td>\n",
       "      <td>-2.095366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.980254</td>\n",
       "      <td>2.510929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.039416</td>\n",
       "      <td>-1.816161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2\n",
       "0  0.983047  3.957817\n",
       "1  1.128419  1.227702\n",
       "2  0.618476 -2.095366\n",
       "3 -0.980254  2.510929\n",
       "4  1.039416 -1.816161"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(1000, 2)\n",
    "data[:, 1] = data[:, 1]*2 + 1\n",
    "df = pd.DataFrame(data, columns=['X1', 'X2'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X2')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(df['X1'], df.X2)\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f673835be80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='X1', y='X2', data=df, kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.07765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>-0.07765</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1       X2\n",
       "X1  1.00000 -0.07765\n",
       "X2 -0.07765  1.00000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X3'] = df['X1'] + 0.5*df['X2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f67382740a0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j1nT8NXHxzAAm9Hdt6VMiOLXiqgpOkiTtBJyu0Ms5AcVX6LXa05XGAmI87RHjaVtXGk9nj6VYluWJnfj6XdZVFZyuJEmSgmVZDujscUDXGguI8bRHjKdtXWk8XWksgj6xNiQIgiB0OSI4CYIgCF2OCE6tW9jZA2iiK40FxHjaI8bTtq40nq40FqEJseckCIIgdDli5iQIgiB0OSI4CYIgCF1OpwYnSZLsJEnaIElSvCRJcZIkje7M8QiCIAhdQ2fXtfkB2CnL8kOSJJkAFp08HkEQBKEL6LSECEmSbIFwwEc+z0FMnDhR3rlz52UdlyAIwhV03uWLrtHrX6vff2fOnHoCRcASSZKGAiHAC7Is1zR9kCRJ84B5AF5eXld8kIIgCJ3ler7+deaekxHgB/wmy/JwoAZ489wHybK8UJblAFmWA5ydna/0GAVBEDrN9Xz968zglA1ky7J86sy/N6ANVoIgCMJ1rtOCkyzL+UCWJEn9znzoFiC2s8YjCIIgdB2dna33HLDqTKZeKjCrk8cjCIIgdAGdGpxkWQ4HRLl6QRCEdqQWVXf2EK4oUSFCEAThKtCgvr7qoIrgJAiCIHQ5IjgJgiAIXY4IToIgCEKXI4KTIAiC0OWI4CQIgnA1uL7yIURwEgRBuBrI11l0EsFJEARB6HJEcBIEQRC6HBGcBEEQhC5HBCdBEISrwPW14ySCkyAIwtXhOotOIjgJgiBcBa6z2CSCkyAIwtVAlq+v8CSCkyAIwlVA5voKUCI4CYIgXCWU11HbDBGcBEEQrhJKtaazh3DFiOAkCIJwlVCJmZMgCILQ1TSImZMgCILQ1ag0IjgJgiAIXYxSJZb1BEEQhC5GLOsJgiAIXY7I1hMEQRC6nHqVCE6CIAhCF1OvVHf2EK4YEZwEQRCuEgoxcxIEQRC6GjFzEgRBELocMXMSBEEQuhwxcxIEQRC6HDFzEgRBELocMXMSBEEQuhxxzkkQBEHocsTMSRAEQehSJEnsOQmCIAhdjIEkoRAzJ0EQBKErMZAkqutVnT2MK0YEJ0EQhKuAoYFEZZ0IToIgCEIXYihJVCmUnT2MK0YEJ0EQhKuAoYFEpULMnARBEIQuxMAAMXMSBEEQuhZDSaKyTgSnK0aSJENJksIkSdra2WMRBEHoqowMDahUqK6bdPJOD07AC0BcZw9CEAShKzM2lADIq1B08kiujE4NTpIkeQJ3AYs6cxyCIAhdnbGh9nKdV1HXySO5Mow6+fW/B14HrFt7gCRJ84B5AF5eXldmVIJwnZNlmfJaJcXV9ZTXKalXajAwAAsTI9xszHCxNsXAQOrsYV7zml7/PLp7YwTklV8fM6dOC06SJN0NFMqyHCJJ0k2tPU6W5YXAQoCAgAD5yoxOEK4f9So10TkVhGWWE5NbSWJBFWnFNdQ2tL63YWVqxNDuttw6wJW7hnTDxdrsCo74+tH0+ufvHyCXSpBZWtvJo7oyOnPmNBa4R5KkOwEzwEaSpJWyLE/vxDEJwjVPlmVi8yo5klTMseRigtJKda0YXG1M6edmw4geDnjam+NiY4aduTFmxoZoZJlqhYq8SgUJ+ZUEpZXywZZYPt0ex4N+nrx4a1/cbEWQulwkCXo4WpJYUNXZQ7kiOi04ybL8FvAWwJmZ06siMAnC5aFSaziZWsr26Dz2xRVQUFkPQF9XK6YFehPo44Cflz3O1qYX9LzJhVUsO57B36ez2BKRy0u39WXW2J4YiiW/y6KvqxUJIjgJgnA1U6k1HE8pYVtkHnviCiitacDc2JCb+jkzob8LN/Z1xtXm0mY6vV2s+ei+wcwd58N7/0Xz8bY4tkTm8cl9gxnsYdtB34nQqL+bDXtiC6hSKLE2M+7s4VxWXSI4ybJ8EDjYycMQhKueLMtE51Tyb3gO/4TnUlxdj5WpERP6uzBpsBs39XPB3MSww1/Xy9GCxTNH8F9ELh9tjeWen48yY5Q3z0zoLfajOlBAD3s0MoRllnNjX+fOHs5l1SWCkyAIl6aoqp4NIdlsCMkipagGY0OJCf1duH+4Jzf1c8bMuOMD0rkkSeLeYR7c1M+Fb3YnsOJkBmuCspg81J1po7wY3t0OSRLLfZfCz8seQwOJ0+mlIjgJgtA1aTQyR5OL+Ts4i90x+SjVMiN62DNnnA8TB7lhb2nSKeOyNTfmw3sHM2tsT/46msrm0Bw2hmbj7WjBTX2dGdnTkWFedrjbmolgdYEsTY0Y7GHLseRiXrm9X2cP57ISwUkQrjIKpZp/wnL480gqKUU12FkYM32UN9MCventYtXZw9Pp6WTJx/f58uakAWyLzGV7VD7rQ7JZdiIDAAdLE/q5WtPH1Ypezlb4OFvSy9mKbiJotWlCPxe+35dIUVX9BSewXE1EcBKEq0RNvYoVJzNYdCSV4uoGBrnb8N2jQ7nTtxumRpd/2e5iWZka8egILx4d4YVSrSEmt5LI7HJicyuJz69ic2gOVU06vFqbGuHraUtgT0cm9HdhsIeNCFZN3DbQle/2JrIvroDHRl67hQlEcBKELq5epWbNqUx+PpBMcXUD4/o4sWB8L8b0crzqLtrGhgYM627HsO52uo/JskxRVT0pRTWkFFUTn19JRFYF3+9L5Lu9ifRzteal2/owcXC3zht4FzKgmzXdHcz5LyJXBCdBEK48WZbZG1fIx9tiySipZZSPA3/M6I+/t31nD61DSZKEi40ZLjZmjO7lqPt4aU0Du2PyWXo8nQUrQ5ky0ov3Jg+8IskdXZkkSTwa0J2vdyeSWlSNj3PXWcrtSF2hKrkgCOdILapm5pLTzF0ejLGhActmj2TN3FHXXGBqi4OlCY+N9GLLczewYHwv1gRl8n+bozt7WF3CIyO6Y2QgsfpUZmcP5bIRMydB6EKUag0LD6fyw74kTA0NePuuATwxpoeuIvX1yNjQgDcn9cfIQOLnA8ncO8z9mk+jbo+LtRl3DHJjQ2g2r97R75qcTV6/v/GC0MVE51Rwz8/H+GpXArf0d2HfK+OZM86nQwOTWiOj1lyd9ZOfu6U3Hnbm/HkktbOH0iVMC/SivFbJtsi8zh7KZSFmToLQyRpUGn45kMwvB5KxtzThjxn+3DHI7ZKfV62RCc0s42BCIaEZ5SQWVFFa24Asg7WZEb4etozr48yDfh64XGIZoyvB1MiQyUPdWXQk9boo39Oe0b0c8XGyZNWpDB709+zs4XQ4EZwEoRMlFVTx0rpwonMquW+YO+/fMwg7i0s7PJtTXseKExlsDsumoLIeIwOJQe423DbQFRdrUyRJori6nrDMcr7YGc/XuxO4d6g7z0zoTa8uvrnu723P74dkkgurGe51/ey/tUSSJKYGevHxtjhicysZ6G7T2UPqUCI4CUIn0GhkFh9L48tdCViZGvH7dH8mDr602VJacQ0/7U/i3/BcAG7u58z/3eXBTf2csWlllpFaVM3Kk5msCcpkc3gOd/p2Y84NPRnWRUsNeTlYAJBVVnfdByeAh/w9+WpXAqtOZfDJ/b6dPZwOJYKTIFxhOeV1vLoughOpJdw6wIXPHhhySSf9y2oa+G5vIqtOZWJsKDFzTA9m39ATDzvzdr/Wx9mKdycP5Ombe/HX0TRWnMhgW2Qe/d2suWWAC6N9nBjS3bbV4HalWZtpL1m1TQ7tXs/sLEy4e4g7/4Tl8NadA7AyvXYu6dfOdyIIXZwsy2wMzeGD/2LQyDJfPOjLIwHdL3qGIssy64Oz+WxHHJUKFVNHevHcLRdXBdzJypQ3Jvbn6Zt68U94LlvCc/n9UCq/HEgBwMPOnL6uVvR0sqKnsyU+Tpb4OFviZnNlSw0ZnekTpZavzqSOy2HaKC82hmbzb3gO0wK9O3s4HUYEJ0G4AgqrFPxvUzR74woY2cOBbx4ZSvczS1QXI6e8jjc2RHI0uZgRPez56L7B9He79D0HazNjZozyZsYobyoVSiKyyonMriA+v4rkwmpOpJagUGqaPN6IAd1sGNbdjpE9HAj0cbisiQqNQcmgCy45dpbh3e3o52rNutNZIjgJgnB+ZFnmn/AcPtgSS12Dmv+7cwCzb7i0TrFbInL53+Yo1BqZj+8bzNSRXhhchs6zNmbGjOvjzLg+Z88UaTQyBVUK0s6UGkooqCI6p5Klx9JZeDgVY0OJG/s4M/dGH0b5OLbx7BdHpdYGJyPRaVdHkiQeGdGdj7bGEp9f2SE3KV2BCE6CcJnklNfx9uYoDiQUMdzLjq8eGnpJVcMVSjUfbIllTVAmw73s+P7RYXg7WnbgiNtnYCDRzdacbrbmjOntpDe20Mwy9scVamu+LTzJXUO68el9vthadNxMSiNmTi26f7gHn++IY2NINv9318DOHk6HEMFJEDqYUq1h6bF0vtubCMC7dw/kiTE9Lmm2lFVay4KVIcTkVvLUTb14+ba+XapqhJmxIWN6OTGmlxOv3tGPhYdT+Wl/ElmltWx6agxGHTTWxqB0tR4kvlwcLE0Y29uJnTH5/O/OAV0y0/JCdZ3fbkG4BpxKLWHyT0f5ZHsco30c2fXijZe8jHc0qZjJPx8ls7SWv54I4I2J/btUYDqXmbEhz9/Sh28eGUZkdgUrTmZ02HPbmGtnYZUKZYc957XijkFuZJXWEZdX1dlD6RBd9zdcEK4iOeV1PLcmjEcXnqRKoeKPGf4seiLgkpIeZFnmr6NpPL74FC7Wpmx59gZuGeDagaO+vCYP6cbQ7na6c1cdwcbMCBMjAwoqFR32nNeK2wa6IkmwOza/s4fSIcSyniBcgpp6FX8cSmHhkVRkGZ6/pQ9Pje+FucmlFeKsV6l5559o1gVnc/tAV759dNhVd4ZFkrSVKXZEdVztN0mS6OloSWpRTYc957XCycqUQe42HE8p4cVbO3s0l+7q+m0XhC5CpdawLjibb/ckUlxdz+Sh7rwxsR+e9hc/U2pUXF3PghUhBGeU8dyE3rx0a9/Lko13JRhIdPj+R/9u1gSllXboc14rxvZyYsmxdOoa1Jd8g9TZRHAShAsgyzIHEgr5bHs8SYXVBHjbs/Bxf/w6qJROVHYF81cEU1LTwM9Th3P3EPcOed7Okl+hwNHy0moFnsvXw5Z/w3MprFJc1IHja9noXo78cTiV4IxSvSMAVyMRnAThPEVlV/DJ9lhOppbS08mS36f7cccgtw6bGfwTlsObmyJxsDBh41NjGOxh2yHP25licisZ0cOhQ5+zscV7RFYFtw0UwampET0cMDSQOJUqgpMgXPPyKxR8uSueTaE5OFia8OG9g5gy0qvDMuYaVBo+2RbLshMZjOzpwK/T/HCyuvhae11FdlkteRUKXTDpKAO6aQ+ZxudVctvAqydB5EqwNDViiKctJ1JLOnsol0wEJ0FohUKpZtERbX05tUZm/ngfnrm5d4cWQc0qreW5NWGEZ5Uz54aevDHp8qeJK5Rq8isUlNY2oFCqkZCwtzTGw868Q0sPnUrV7guN7tWxlSIsTY3wsDMnpai6Q5/3WjHKx5E/D6dS26DCwuTqvcRfvSMXhMtof3wB7/8XS2ZpLZMGu/G/OwdcUlp4S3ZG5/P6hghkGX6Z6sddQ7p16PPLskxuhYKo7HJiciuJya0kIb+K3Io6WqqbKknQ18WaSb5uPD66Bw6XuFd0IrUEewtj+rlaX9LztMTdzoy8CpFO3pJRPo78djCF4PSyq7qdvQhOgtBEfoWC9/+LYWdMPr1drFg9J1CvTE9HqGtQ8+FWbRkiXw9bfpnqh5fjpQc+jUYmsbCKkyklBKWXEpxeRmFVPQCGBhK9nC0J6GGPj1N3POzNcbQ0wczYEFmWKatVklxYzam0Er7fm8RfR9P45uGh3H4JHXmD00sJ6OFwWTIN7S1MyCip7fDnvRYEeNtjZmzAntgCEZwE4WonyzJ/n87ik21xNKg1vHZHP+aO88HEqGOX2MKzynllXTipxTUsGK8tQ3Sxr1FTryI8q5zg9DLCssoIzyqnvFZbOcHDzpwxvRzx87ZniKcd/d2sMTM+n9TiPiQWVPHKugjmrQjhiwd9eXSE1wWPraJWSXpJLY+M6H7BX3s+zIwNqVepL8tzX+0sTY24dYAr26LyeHfywC5dTaQtIjgJ1738CgWvb4zkcGIRo3wc+OLBIR1eULVBpeHn/Un8fCAZNxszVj4ZyNgLnJGV1zYQmlnGqdRSTqaWEJ1biVojI0nQx8WKOwa6EdDDntG9HC/pvFVfV2vWLxjNnGXBvPdfDP7eDhdcsDa1WLsf1Nel45f0QFtbr6Pq9V2L7h3mwdbIPI4kFTGh/9WZNCKCk3Bd2xmdxxsbo2hQafjo3kFMC/Tu8GWomNwKXlkXQXx+FQ/4efD+PYPOK6lCrZG1lb7jCzmcWERsXiWyDMaGEsO72/PU+F6M6OnAcC+7Du9Ua2ZsyLePDOXWbw/x+Y54Fj0RcEFf37ic6GZ7eVK9axpUmJ/XTPD6NL6vM05Wpiw9niGCkyBcTRRKNR9vi2XlyUyGeNry/aPD8HG++HYWLWlQafj9UAo/7U/CzsKEPx8PaDf1Wa2ROZlawraoPPbEFlBUVY+RgYS/tz0v3tKXQB8HhnraXZHT/y42Zjx5gw/f7U0kpaiaXhfw/jSG98vVsLa0pgH7Dj7cey0xMTJg9g09+HJnAqGZZR12SPxKEsFJuO5kldby1KoQonMqmXejD6/e3q/D95aazpYmD3Xnw3sGtXkxzS6rZW1QFhtCssmvVGBhYshN/ZyZOLgbN/Vz7vCZ0fl6bGR3vt+XyNaIPF64tc95f11jZmNiQRW+nh1/mDivQkF/t8uzZHiteGJ0D5YcS+ejrbFsXDDmqiuBJYKTcF05mlTMs2tCUWvk85rJXKgGlYYf9yXx+6EU7C3bni3JsszBxCKWHU/nUGIREtrlmLfvHsAt/V27RG00VxszArzt2RmTf0HBqZ+rNY6WJmwIyeYBP48Ora+nUKopqqrvkDqG1zJLUyPenNifV9ZHsPxEOjPH9uzsIV0QEZyE64Isyyw9ns7H2+Lo7WzFHzP86eHUsUkP0TkVvLYhkri8Sh708+Ttuwa0OFtSqjVsicjlj0OpJBRU4WJtynMT+vBIgGeXvODe3N+FL3cmUFipwMXm/PaQDAwkXrytL+/8E82SY+nMvqHjLozZZXUAdHcw77DnvFY94OfB1shcPt8Zz6hejldVC3cRnIRrnkqt4f0tMaw8mXlZ2k80zpZ+O5SCfRt7S0q1ho0h2fy0P5mc8jr6ulrxzcNDmTzUvUOWFSsVSpIKqkkvriGnvI7SmgZq6lVIkvZckJejBYE9HenlbHlBM5kb+zjz5c4EjiQV86C/53l/3bSRXuyNLeDDrbFE51Tw7uSB2Flc+j5Rbrk2OLnbiuDUHkmS+OKhIdz941EWrAjh32dvwNa8c5aIL5QITsI1rbpexTOrQjmUWMSC8b14/Y5+Hbr2HptbySvrI3SzpXfvHoithf5/fo1G5t+IHL7fm0RGSS1Du9vx0X2DuLmfy0Utd2k0MhmltcTmVhKTW0FcXiXx+VXNKiZYmxlhZWqERpYpq1HSoNYAMLS7HW/c0e+8DxcP7GaDo6UJR5MvLDgZGEgsnjmCn/Yn8eO+JPYnFPLq7f2YFuh1Sct8eRVngpOdCE7nw8XajF+n+THlz5PMXxHMstkjMTXq/CXj9ojgJFyzCisVzFp6mvj8Kj5/wJfHRl74YdLWqDUyi46k8s3uRGwtjFk4w7/FagonU0v4eFss0TmVDOxmw6LHA7hlwPkHpYo6JUkFVSQWVJOQX0lsXiVxeVVU16sAMDKQ6O1iRWBPB/q52dDbxYpezpa425nrHbqVZZnM0loOxBey8HAqUxedYu64nrw1aUC7wdrAQOKGPk4cSSpCo5EvKLgbGki8eGtfJg5248Mtsbz9TzS7Ywv46bHhzYL4+So6k6bubH31F8e9UgJ6OPD1w0N5YW04L6wJ56epw7v84VwRnIRrUnJhNU8sDqKstoFFTwRwcz+XDnvutOIaXl0fQUhGGXcMcuWzB4Y0q0OXXVbLR1tj2RVTgLutGT88NozJQ9zbvLAXVioIzSwnKkdbCy8+r4r8Ju3ILU0MGehuwwN+Hgxyt2FgN1v6uFqdV+UHSZLwdrRk5tiePDbSi8+2x/HnkTQUSg0f3juo3WA5vq8z/4bnEp1bwRBPu3Zf71z93WxYNSeQlScz+GBLLG9tjuSXqX4XNYMqq1ViYWJ4nhUvhEb3DvOgtKaBD7bE8syqUH6e6tfhWaodSQQn4ZoTmlnG7KWnMTKQ+Hve6A5LZZZlmTVBWXy8LRZjQwO+e3Qo9w3Tz0RrUGn480gqP+9PBuDV2/syZ5xPixfSSoWSQwlFHEkq4mRqKZml2lpxjbOh0b0c6edmTT9Xa/q4WuFua94hS5Jmxoa8f88gTIwM+PNIGoM9bNotUXRTPxckCfbFFV5UcAJtgJwxugc1DWo+3xHPjuh87vS98GK3V3u17c40a2xPJOD9LbE8vSqEX6b5ddklvk77CUuS1B1YDrgCMrBQluUfOms8wrVhX1wBz6wOxdXGjOWzR3ZYGaLSmgZe3xDJ3rgCxvZ25OuHh9LtnA350Mwy3tgQSVJhNRMHufHO5IF4nLMvolCq2RWTz7/huRxJKkKplrExMyLQx5EZo7zx87ZnkLvNZZ8VSJLEm5MGEJNbyQdbYhnl49jme+VgaUKAtz27Ywt46ba+l/Ta88b5sPx4OptCsy8qOGk02gAuXJyZY3tiaCDxzr8xzFkWzG/T/Ts0QaijdOaIVMArsiyHSpJkDYRIkrRHluXYThyTcBVbdzqLtzZHMbCbDUtmjeiwhn374gp4Y2MUlXVK3rl7ILPG9NCbwdQ1qPlqVwJLjqfRzcaMv54I4JYB+tl6WaW1LD2ezvrgLCoVKtxtzZg1tie3D3RluJc9hhdwsa1XqckuqyO3vI6CynrKaxtoUGswkCQcLEzwdrRgaHe7dgOcoYHE1w8P5fbvDvPuvzEsnTWizWW2Owa58fG2ONKLay4pDd/AQOLGvs7sism/6K9XaS5T6YnrxIzRPTA1MuStzVE8/PsJlswccdlKTV2sTgtOsiznAXln/l4lSVIc4AGI4CRcEFmW+fVgCl/tSmBcHyd+n+6PZQfcCdbUq/h4WxxrgjLp72bN8tkjGeiuf04kPKucl9eFk1pUw4xR3rw+sZ9ew76kgip+PpDMlohcDCSJiYPdmDLSi9E+ju0u0SmUapIKqkkoqCKxoIqkgiqSi6rJLmu5H1NTJoYG3D7IlRdv7UPvNoqvutuZ8/Jtfflwayy7YvKZOLj1mcydvt34eFsc/4bnXtCB3Ja4WJtSXqe84AQL0O691ZxJCBEu3iMjuuNqa8bTK0O4/9djLJ45QtdluCvoEnM5SZJ6AMOBUy18bh4wD8DLq+OyrYRrg1oj8/5/Maw4mcF9w9z58qGhHbLJG5ZZxot/h5NZWsu8G3145fa+emvzKrWGXw6k8OP+JFytTVn5ZCA39Dmbmp1TXsc3uxLYHJ6DubEhc8b5MHtsz1bvThtUGmLzKgnPLCMyu4Lo3ApSimpQn5khmBgZ4ONkyVBPO+4f7kkPRws87S1wtTHFztwEU2MD1BqZ0poGEguqOJJUzPrgLHbF5PPu5EFMbyN9+/HR3qwLzuLjbXHc1M+l1RmXu505o30c2RSWzfO39L6kdHBJki667p6NuTF1SjUNKk2X3tDvCJf7+je+rzPrF4xh9tLTPPz7CX6d5tdlekB1enCSJMkK2Ai8KMty5bmfl2V5IbAQICAgQMzlBR2FUs0La8PYFVPA/Bt9eGNi/0tOGGh6oNbNxoy/541mZE8Hvcdkl9Xy4tpwgjPKuH+4tsp448FGhVLNrwdT+ONQCqDdX5k/vlezbD6NRiY6t4JDCUUcTykhNLOMepX2HJKztSm+HrbcPtCNge429HOzpoej5Xkt/VmaGtHdwYJbBrjy3ITevLo+gnf+iaa0uqHV2Y6RoQHv3D2QaYtOseJEBnNv9Gn1+R8O8OTldRGcTC29pPbrlQolliaGF/Xzsj+Tgl5e14CLdddaiupoV+L6N9Ddhs3PjGHWktPMXnqaT+/3vWx9uC5EpwYnSZKM0QamVbIsb+rMsQhXl7KaBuYuDyYks4x37x7YIeVxmpYfesjfk3cnD2xWcHVXTD6vrde2Vv/hsWHcO8xD97lDiUW8/U8UWaV1TB7qzhsT++mVI5JlmcjsCjaH5bAzOp/8SgWSpD3kOi3Qm4Ae9gz3ssPNxqxDatE5Wpny1xMjeHVDBN/tTaSHk4XeeJsa29uJcX2c+PVgMo+N7K63NNnUpMHdeO+/GNYEZV5ScMorV1z0HofNmRuBKoWKy9Qu6rrTzdac9QtG8/SqUF7fGElWWS0v39a3Q2siXqjOzNaTgL+AOFmWv+2scQhXn6zSWp5YHER2WR0/T/HjriEXnvHVVGMjwF8Otl5+qEGl4fMd8Sw+lsYQT1t+nnK2tXpFnZIPt8SyMTQbH2dL1swdpXfhrmtQszE0m5UnM4jPr8LEyICb+jrz+uB+jO/rjGMriRtqjUx2WS3pJbXklNVRVFVPlUKJUq1dznKyMqWXsxUjejq0WpLGwEDi8weGkF1ax5sboxjkbttq48BXb+/Hvb8cY/WpTOaP79XiY8xNDHkkoDtLj6fz2h39dNXHL1R6SQ1eF/m1jX2c6hpEJ9yOZG1mzOKZI3h7czQ/7U8mu6yOLx4c0mlLp505cxoLzACiJEkKP/Ox/8myvL3zhiR0dZHZ5cxeGoxSrWHlnMBmS24XKjSzjP9titI1Anzv7kHNKhfklNfx7OpQwjLLmTmmB2/d2V+3/3Q8pZhX1kVQWFXPMzf34rkJfXR7NrUNKpYeT2fRkTRKaxoY5G7Dp/f7cteQbs2CiSzLZJTUcjq9lLCscmJyKojPr9It9TUyNzbExMiAepUahVL7OWNDiXuGevDWnf1bzFA0MTLgp6nDmfTDEZ5bE8bmp8e0uK80tLsdo30cWXY8nTnjfFpdRpwzrifLT6Sz6EgqH9w7+Dzf6bNUag2pxTUXvbdhcOZuXnO5mkVdx4wNDfj8QV+6O5jz9e5E8irq+GN6wEVX87gUnZmtd5SzPckEoV374wt4ZlUYDpYmrJkbSB/Xi1/TqVIo+XpXAstPZuBq3XL6N8DBhEJe/DsclVrm56nDuXuIO6At4vrtnkR+P5RCT0dLNj41hmHd7QDtjGft6Uy+25NIcXUD4/s68/RNvRjZ00FvmaSuQc2hxCIOxBdyOKlIVxvP2swIXw9bpo/ypq+rFT2drPCwN8fZylTvLraiTklCfhXbo/JYdSqDPbH5/DBleIvVMFxtzPjqoSE8uSyYz3fE8/49g1p8X6aP8uaZ1aEcSy5uNXh0szXnnqEerA/J5uXb+l3whSutuIYGleai+zHVNGgz9Sy6QEuRa5EkSTw7oQ+e9ha8tiGCR/44wfInR+J6nhXpO0qnJ0QIwvlYeiyND7fGMtDdhsUzR1zSRvjO6Dze+y+Gwqp6Hh/lzat39Gu2x6LWyPywL4mf9ifRz9Wa36b70/PM2Z68ijqeWx1GcEYZj43ozruTB+oqFoRnlfN/m6OIya1kZA8H/pjRH3/vs11INRqZo8nFbAzNZndMAXVKNdZmRtzQ24mnb3YisKcDvZ2tzitRwNbcmJE9HRjZ04Hpo7x4fk0481eEsGpOICN6NJ9R3jLAlZljerD0eDrj+zm3GMRuHeiCtZkRWyNz25zZPHlDTzaGZrP2dOtLgK2JyqkAaJaWf75KqhsAbaV14fK5b7gHztamzFsezAO/HmfFkyM7vFt0W0RwEro0tUbmo62xLD2ezq0DXPlxyrCLLl2TVVrLh1tj2RNbwIBuNvwxI0A322mqrKaBF/4O53BiEQ/6efLxfYN1jf+OJxfz7Jow6pVqfpwynHuGamdSCqWab3YnsOhoGi7Wpvw8dTh3+XbTzZRqG1T8fTqLpcfTySipxdbcmPv9PLjLtxsjezpcchHO3i7WrJwTyIO/HWfe8mC2vzCuWQULgDcn9edESgmvrY9kz0s3Nus3ZWpkyPi+zuyPL2zzDNJAdxsCezqw/EQGc8f5XFDWXXhWORYmhvS5yGyGtOIarE2NmmVACh1vbG8n1s4bzcwlQTz0+wmWzhpx0eWrLpQITkKXVaVQ8sLacPbHFzLnhp68deeAC6qk0Eil1rD4WBrf7klEQuKtSf2ZfUPPFgNCZHY5T60Mpaiqnk/uH8zUkV5nzuTI/Hkklc93xNPL2YrfpvvrEgvi8ip5YW0YiQXVTAv04s1J/XUzMYVSzYoTGfx6MJmyWiX+3va8cns/7hjk2mJNs+p6FQn5VeSU11FSXU+9SoOFiSEu1qb4edu3OWN0sDThrycCuPuno7y4NpzVc0c1e7/MjA35/rFh3P3TUb7clcBnD/g2e55xfZzYGplHanF1mwd4HwnozivrIy64GOzp9DKGe9ld1M8SICK7nIHuNp2aSXY98fW0ZcNTY5jx1ymmLDzJHzMC9M70XS4iOAldUk55HU8uPU1SYTUf3zeY6aO8L+p5onMqeGNjJDG5ldw6wJUP7x3UYh8gWZZZcTKDj7fG4WxtyvoFoxl6ZlZV16DmjY2R/BeRy52+bnz10FAsTY2QZZm/T2fx7n8x2Jobs3TWCG46s1QmyzI7o/P5eFscOeV13NjXmecn9CbgnOW2mnoVR5KKOZRYRFBaCSlFNW1+Pzf0duLFW/s0e55GPs5WfHDPIF7bEMnP+5NbPNs0oJsNT4zuwZLjacwZ15Ne5yzVND53aGZ5m8Hp5v7a7/VocvF5B6eS6nri8yt56daLq89XVtNATG4lT990YUuJwqXp6aTdV31icRCzlgbx05ThbVYT6QgiOAldTnhWOXOWBVOvVLN01gjG9bnwrK6m6eEOlib8Ns2PiYPdWrzbrlQoeWtjFNui8ri5nzPfPjJMt9yVW17HvBXBxORW8tod/Xj6pl5IkoRCqeadf6JZH5LNDb2d+P6xYbpMufwKBf+3OYp98YX0d7Nm9dxAxvQ6e6ep0cgcSS5mXXAWe2MLqFdpsDY1YkRPB+4f7kF/Nxu8HC1wsjLF1MiA2gY12WW1HE0qZtmJdB76/QTPT+jNS62cQ3nI35PjKSV8vy8Rf2/7Fu9yn765F6tOZbDwUCpfPDRE73M9HC0xMzYgIb+qzffYwdKEbrZmJBVUt/8DOeNIUjGyzEVn6u2JK0CtkVvsNCxcXq42Zvw9fzSzlgTx7OowfpoCky6icO/5EsFJ6FK2ROTy6voIXG3MWDsvsM0799YkFVTx/Npw4vIqW00PbxSZXc6zq8PIKa/jzUn9mddk/yQ0s4x5y0NQKNVnmgRqL4iFlQrmrQghPKuc5yf05oVb++qWqLZH5fHWpigaVBrevmsAM8f0wOjM8mG9Ss2m0BwWHk4lrbgGB0sTHgnozp2+3QjoYd/qvpOlqRHO1qYM97Jnzjgf3vsvmh/3J2NiZMCzE5rPjCRJ4uP7BhOTW8Gza0LZ8uwNzc4jOVmZcs9Qd7ZH5/Hx/YP1XtvQQKKHoyUZJbXtvtdutmYUV9e3+7hGu2LycbLSVsC4GOuDs+jhaHHRXy9cGltzY5bNHsnMJad5bk0YP3H5ApQITkKXIMsyP+5L5ru9iYzoYc/v0/1bPZzalr9PZ/LuvzFYmRqx6PEAbm3lDluWZRYfS+fzHXE4W5mybv4o/L3PLpVtDMnmrU1RuNma6aWtx+dXMnvJacpqlfw2zU/3H7NepebjrXGsOJnBUE9bfnhsuK5yt0Yjsyksh+/2JJJTXscQT1t+eGwYEwe7XXAvHXMTQz5/YAhKtczXuxPx87bXm5U1sjQ1YuGMAO75+ShPrQph41Njmr3WLQNcWR+STVhmebPzYt1szXTt0NtiYmhAvVLT7uNAm/q+P76QRwK6X9R+U2xuJafTy3hrUn+x39SJrM20S9gzl5zm2TVh/GogcUcLXaAv1bVdNVG4KtQ2qHh2dRjf7U3kAT8PVs4JvODApFCqeW19BG9sjGJEDwd2vDiu1cBUUl3PnGXBfLQ1lvF9ndn2/DhdYFJrZD7dHscr6yPw97bn32fG6gLT4cQiHvrtBGpZZv2C0brAVFip4LGFJ1lxMoO543qy4akxusAUnVPBA78d59X1EThYmrBs9kj+fWYs9w7zuOgmbwYGEp/e70sPRwte3xDZaoXuHk6WfPPIMKJzKvl2d2KzzzdmKsbnNytpiZ2FCZUKZbtjqa5XYWV2fve4m0KzqVdpePQi67b9cjAZK1MjHmunMaJw+VmbaWdQvh62PLc6jOPJxR3+GmLmJHSqzJJa5q0IJqGgijcn9Wf+jT4XfFfcdJntuQm9ebHJMtu5DiUW8er6CCpqlbw/eSBPjOmhe72KOiUvrg3jQEIRM0Z58+7kgbrlrg0h2by5MZI+rtYsnhmgS9OOya1gzrJgymuV/DrNT9c8r0Gl4Yd9ifx+KBV7C2O+eXgoD/jpd81NK67hcGIREVnlpBRVU1rbgKEk4WxtymgfRyYPdW/1oLG5iSFfPTyUR/44wZc741ut1HDbQFce9vdkybF0Zp1TFd3VRrunlV3WfIZkZmxIXUP7M6K8CsV5LbGp1BqWHEtnuJcdgy9iSS4qu4JtkXk8c3OvTqlWIDRnZWrE0lkjeOSPE8xdHszquaN0SUQdQQQnodMciC/k+bVhGEgSS2eNZPxFbJInF1bzxOIgymob+H26X6sZRPUqNV/vSuDPI2n0dbVi+eyRer1rkgurmbc8mMzSWr3swKa9osb2duT36f66NPHDiUU8tTIEG3NjNjw1mkHu2otuSlE1L6wNIzqnkgf9PHn37oG6C2p1vYqNIdn8fTqL2DztjMXZ2pT+btb0dLJEI0NWWS0/H0jmpwPJPDW+Fy/f1le3b9XUiB4OPDFae6h2km83Rvm0XIj1+Vv6sCksh6XH03lzUn/dxyVJwtrMmCpFSzMvmfbuEQqrFJTWNJxXpY6tkXlkltbyf3cNaPexzUYiy3y8LRYHS5MLPvArXF52FiaseFJ7vm7mkiA2PjWmww7qiuAkXHEajcz3Z6ovDHCz4Y8Z/hdVQDQqu4IZi09hZCDx97zR+Hq2fEeeXlzDc2vCiMqpYPooL96+a6BebbldMfm8si4CUyMDVs8dpdt/aatX1L/hObyyLoI+rtZ6XUS3RubyxoZITIwM+GOGv24tvqJOyV9HUllyPJ0qhQpfD1vevXsgtwxwabE9enF1PV/tTODXgynkVyj45pGhLc4oX5/YjwMJhbyyLoIdL45rVkUdoLuDBYE9HTiYUKgXnLTfo6bFludVClW7rbtPp5UBMNzLrs3HKZTaTsEDu9lwWwslotqzMTSHU2mlfHq/b4vfn9C5XG3MWHkmQM1eeppNT4/tkAPSIjgJV1RFrZKX1mkP1p5bfeFCRGaXM33RKazNjFk9N7DFCzxog8j/bY7G0EBi4Qx/bm+ycavWyHyzWxsAhnra8tt0f90ZqKa9oubd6MObTXpFLTmWxgdbYhnl48DCxwOwMTNGrZH5cmc8fxxOxc/Ljl+n+eNma4ZKrWHVqUy+3ZNIRZ2SSYPdmD++V4uVKZpysjLli4eG4G5nznd7E/HvYc+0wOZnvSxMjPju0WE8/PsJPt4ay5cPDW3x+QK87flxfzJKtUa3VKnWyFQpVFi3sGdUXF3f7gXmYEIh1qZG7S7r/Xk4lZzyOr56aMgF928qqFTw0dZY/L3teawL9BgSWtbDyZKFjwcw5c+TzF8RzMo5gRe9p9pIBCfhiknIr2Lu8mDyKur48N5BzBjlfVFZV4kFVcz4KwhbC2PWzB2l1zOpkUKp5oMtsawJyiTA254fpgzHo8nh25Lqel78O5wjScVMGdmd9yYP0s2mymoamLM8mNDMMt65eyBPnukVJcsy3+9N4od9SdwxyJUfHhuOmbEhVQolz685u1f1zt0DMTEy0DsAPKaXI/+7c8AF77c8N6E3wRmlfLItjlv6u7bYA8nPy575N/rw68EU7hvu0WL2nt2ZOnTVCpXeGS6VRm5x1ppRUttmv6Z6lZrdsQXcOtC1zdJLmSW1/HIwmTt93RjT+8KqCsiyzJsbI1Eo1Xx5EYFNuLL8ve355uGhPLcmjLc2RrU62z9fIjgJV8SumHxe/jscC1Mj1s4brVcM9ULkltcx469TmBoZsOrJlgNTTnkdC1aEEJVTwYLxvXj1dv09m5CMMp5dHUpJTQNfPOjLo02yv9KKa5i1JIjcCgW/TD2b4KDRyHx4psbfQ/6efP6AL0aGBmSV1vLkstOkFtXwyf2DmRbojUqt4fu9ify0PxkHSxN+nebHpFYOALenMTNvwjcH+WFfUovlhkC7r7QhJJvfD6W2GJzqlNreR02XM2NytQVY+51THbyoqp68CkWbVcN3xRRQUafkvuEtNy8E7Xv26oYIjA0MePuuga1/k61YciydAwlFvD95YLMqFkLXNHmoO2nFNXy7J5Gh3e14YkyPi34uEZyEy27RkVQ+3hbH0O52LJzhf9Gl92vqVTy5LJjaejXrFozWNftrKiSjlHnLQ6hXaZo1DZRlmSXH0vl0exzuduZsemqM3kzmREoJC1aGYGggsWZuoC69XKnW8MbGSDaF5vDkDT35vzsHYGAgnalkcZoGlYZls0cytrcTWaW1PLcmjPCscm0L98n6B4DrVWoisytIL66hUqHC2doUf297vVndubo7WDB1pBcrT2Xy7ITeLT7WzNiQqYFefL83icIqRbMafFmltdhbGOstoZ5MLcXEyIBB51QHD0orBWjzBmL58XS6O5gzro3Z0OJjaQSllfLlg0NaLBnVltDMMj7bEcetA1wu6QInXHnP3tybyOxyPtoayyB3m1ZLbbVHBCfhspFlma92afd0Jg1247tHh7XY5O58n+u1DREk5FeyZJZ+pl2jLRG5vLI+AndbM/5+IkCvukSVQsnrGyLZEZ3PbQNd+frhoXoN/9YEZfLOP9F4O1qweOYI3R6WQqnm2dVh7I0r4OXb+vLchN5IksTumHyeXxuGs7Upa+eNoreLNXtiC3hlXTiyDD9NGc7kMxXLZVnmZGopq05lsDeuQNcksJGBBHcMcuOzB3x1y2/nmje+FytPZbL4aBrv3N3yLKQxkSM+r6pZcArOKNNL85Vlmb1xBdzQ26nZ3sC++ALsLIwZ2kq9vKC0UoIzynh/8sBWl9qicyr4Ymc8tw905eEAzxYf05rCSgVPrQzBzdaMrx++tKUh4cozMJD45pFh3PvzUZ5eFcr2F8a12ASzPSI4CZfN12eSDaaM9OLj+wZfdBVqgMXH0tkelc9bk/q3mHK+7Hg67/0Xw4ge9iycEaDXCiKxoIoFK0LIKK3lrUn9mdfkLFWDSsNHW2NZcTKDG/s689OU4bqgVVGrZM7y0wRnlPHRvYOYMbqH7rXe3xLDEA9b/po5AnsLE77cGc+vB1MY7GHDr1P9dbO6I0lFfLM7kfCscmzNjXnI35NxfZzp72aNjZkx+ZUKtkTksuhoGjOXnGbVnEAsW8iS87AzZ+JgNzaGZvPGxP4tts5u7G9Ufc6h3NSiapILq/USCkIyysguq+OFW/TLH9U1qNkTU8Dtg9xaTF/X3nDE42xtqrcc2lR1vYpnV4fiaGnKFw8OuaDgolCqmbsihMo6FRufGtNqsBa6NltzY36b7s+9vxzjrU1RLJzhf8E3GSI4CZfF4qNp/HJAG5g+uW/wJW1mh2eV89n2OG4f6Mq8G32aff6XA8l8tSuB2wa68tOU4Xqzs53Reby8LgILEyNWzwkksMlZoMJKBU+vCiU4o4y543ry5qSzLTmyy2qZteQ0GSW1/DzFj7uGdEOjkfliVzx/HErl1gHa12pQa5iz7DQHEop4bER33r9Hm1iRVVrL+//FsC++EE97cz65fzAP+nk2mznaW5owoJsNQ7vb8dTKEN7+J5rvHh3W4vvwkJ8n2yLzOJZS3GKjwKIqbY07u3MOqW4KzUGS0HXxBVh5MgNLE0PdnlqjrZG5VNWreMi/5dnO7tgCTqeXtZplKcsyr2+IILO0lrXzRjfrF9UWjUbmlXURRGSV8/t0/4tuRih0DQO62fD6Hf34eFscG0KyeTjgwrItRXASOtz++AI+2hbLHYNc+fgSA1N1vYrn14SdaTPefInn5/1JfL07kfuGufP1w0N1d/uNZ6l+3JfEcC87fp+uv9d1KrWEZ1aHUVOv0msaCNo09SeXBaNQqlk2eySjezlSr1Lz6vpItkTkMn2UF+9PHkRmaS1zlgeT1eTgrkYj8+fhVL7Zk4ChpO0dNXNsj3bTau8Y5Ma8G3vxx+EUnp3Qu8UEAG2bd4jIKm8xOAWnl2IgoTsMDNqZ0KpTGXqZflmltWyJzGPmmB56szSNRmbh4VT6u1kzyqf5PkFdg5oPt8TSz9W61RJEi46k6Wa459bra8+n2+PYFpXH/+7sz8TBHV+rTbjyZo/tyZ7YAj7YEsvY3k4XtPcoausJHSq3vI6X/o5gYDcbvn90+CUt5QF8vDWW7LJavn9sWLOyNX8dTePr3YncP9yDbx4Zplf9+4W/w/lxXxIP+Xuydt4oXWCSZW3wmLroFDZmRvzzzFi9wLQjKo9H/jiBiaEBm54aw+hejlTUKpnxVxBbInJ5Y2J/Prp3MKfSSrnvl2OU1ypZNWcU00d5k1+hYNqiU3yyPY4bejux5+XxzB/f67zPe8wZ1xNDSWJ9cHaLn7c0NcLNxqzFckOyLLM1Kg8/L3u9vbSlx9Mpq1WyYPzZGef3e5MwNJCYM66n3nP8F5FLUmE1T9/cu8UlmG92J5BTrj0G0FL6+MGEQj7bEcekwW4tznDb8sehFBYdTeOJ0d7MHXdhXyt0XQYGEl8/PBSlWsOXO+Mv6GvFzEnoMBqNzItrw1GpNfwy1e+iDtc2dSixiLWns1gwvhcjzsn4+Scsh4+2xjJxkBtfPTREFwQrFUrmLQ/mZGopr0/sx1Pje+kutDX1Kl7bEMH2qHwmDXbjy4eG6EoRybLMz/uT+WZPIn5edix8PAAnK1OySmuZtfQ0mSW1/PDYMO4d5sHaoEze/icaH2dL/npiBN0dLDiYUMhLf4ejUGr44kFfHgnofsFr7E5WpgT6OLA/vqBZJYdGsgwtPevR5GJSi2p4+uHeuo8VVdXz68FkbunvosuYis6pYFNYNvPG+ei1ca9rOFvF4e4WWiAEp5fy17E0po/y0lsabZRSVM3za8Lo52ZzwUkMf5/O5LMd8dw9pBvvTR4kEiCuMd0dLJgzrie/HEhh5tie7R5AbySCk9BhVgVlEpReylcPDdFV5b5YtQ0q/m9zFL2cLXnxnG6up1JLeG1DBKN8HPhhytkZU1lNA48vDiIur1IXSBqlF9cwb0UwyYXV/O/O/swddzYpoq5BzWsbItgamcf9wz347AFfzIwNicquYNbS0zSo1Cx/ciQjezjw2Y44/jiUyvi+zvw8dTiWJkZ8vzeRH/Yl0c/Vmp+n+unat1+M4d3t+e1Qil4lh0bV9SoKqhTNznbJsszXuxLoZmvG3UPOBpaPtsZSr9Tw1p3aenZqjcz/NkfhaGnK0zf31nuOH/cnkVNex9cPD222DFupUPLC2nC621vw5qTmtfHKahqYteQ0xoYGLJzh32JCR2v+Dc/hzU1R3NhX2+RRHLS9Nj11U2/+Pp3FJ9tiWTd/9HndgIjgJHSI4up6vtwZz5hejq1upl+Ib3cnkl1Wx7r5o/WSCLJKa1mwMoTuDhb8MT1At2RWXtvAlD9Pklpcw8LH/ZnQ/+z5psbzS5IEy2cH6nWGbdrp9o2J/VkwXhu09sYW8NyaMBwsTVg7LxBPewueWxvGtsg8pgV68cE9g6hXaXhqVQi7Ygp4YLgHn9zve8mzRQ97c9QamaKq+mbr86dSS5BlGHZOLbsNIdlEZFfw5UNDdO/Vzuh8/ovI5cVb++iC5R+HU4jMrtDLSATtHtbCw6k85O/ZrCqELMu8tj6C/EoFGxaMblZvT6FUM2d5MPmVCtbOG3VBNRK3Ruby8roIAns68Md0/xYzEIVrg5WpEc/f0od3/40hJKPsvM4+ieAkdIivdyWgUKr58N7Bl7wsE5ldzuJjaUwL9NLbVFco1cxfEYJaI/PXEyP0Kn0/vjiI1OIa/noiQK+t++awbF7fEEkPR+0SXNODu611ul1xQpuWPtjDlkVPBGBqaMjjfwURlF6qm3UVVtUza8lp4vMreefugcwe26NDlqM0sgzQ4l7dxtBs7C2MGd1kWa2oqp5PtscR4G3PQ37am4L8CgVvbYrE18OWp2/SzpCisiv4bk8id/l205td1dSreOnvcFytTVs8P/XnkVR2xRTw9l0DGO6lfyhXfWYZNzSzjF+m+uF3zufbsi0yjxfWhuPnZcdfT4y45KAudH0P+Xvyze5Elp/IEMFJuDIySmpYH5LNjFHel7SkBdoL3jv/RONoZcob5+y7fLAllti8ShbPDKDnmWVDpVrDUytDiMmt5I/p/nqB6a+jaXy0NZbRPo78PsNfb7bwT1gOr2+MxM3mbKfbpqnit/R34aepw6moU/LwouOkF9fqDtYmF1bxxOLTlNc28NfMES1mzl2s3PI6jAykZungKUXV7IzOZ+44H90MQ5Zl3tgYSW2Dms8e8MXAQEKp1vDM6lDqVRq+f2wYJkYGVCqUPLM6FCcrUz6+7+zNgyzLvLUpivSSGlbNGaX3/gAcSCjk8x3x3Onrpqsv2EiWZd79N5qdMfm8e/fAZinpbdkcls0r67TNHJfOGnlBy4DC1cvCxIg7fbvxb3gOdQ3qdm9IxDxauGS/HUzB0EDiqZsuvdfO2tOZRGRX8PZdA/TaI+yMzmNNUCYLxvfSW7J7/78YjiQV89n9vnqdb387mMJHW2OZNNiNpbNH6C682uKtibz4dzjDu9vpOt0q1RpeWR/BH4dSmRboxR8z/MmvUPDQbyfILVewdNYIJg91JzK7nId+P0G9SsPf80d3aGAC7Zmu3i5WzTL8vt2TiImRAXObZMEtPpbO/vhC3prUX9dT6eOtsYRklPH5g0Po5WyFRiPz8t8R5JbX8fPU4XrnjpYcS+e/iFxeub1fs+W8hPwqnl/deoLDN7sTWXUqk6du6sXscwJXW1afyuTldRGM8nFk2WwRmK43dwxypbZBTUhGWbuPFb8ZwiUpqFSwMTSbx0Z4XXTNvEZlNQ18tSuBUT4OeundhVUK3toUha+HLa/c3lf38bVBmaw6lcn88T480uTczfIT6XyxM557hrrz7SNnzz6p1Bre2hTF+pBsHvL35NP7fTExMqC2QcWClaEcTizitTv68fRNvYjLq+LxxaeQZVg7bxSDPWwJTi9l1pLT2FoYs3rOqBZr+13q938ytbRZGvax5GK2Rebx0q19dWVggtNL+Wx7HLcNdGXmmdpza4IyWXYigzk39NS9f9/uSWRvXAHvTx6oqxUI2soVn5w52PzUOQ38CisVzF56GnMTQxY9EYCFif5l4o9DKfx8IJnHRnTn9Tv6nff3t/BwCp9uj+fmfs78Nt3/oktZCVcvf297JAnCMsv09n5bIoKTcEk2hGSjVMvNln0uxle7E6hSqPjgHv2lp//bHE1Ng5rvHh2qy2BLyK/ivf9iuKG3E6/fcXb5b1dMPu/9F8NtA131AlO9Ss0La8LZGZPPC7f04cVb+yBJEpUKJU8uPU1IRpmuQnl0TgXT/zqFubEhK+cE0svZitDMMp5YHISrjRmr5gbqpWF3lNVBmag1MvcOOxuYa+pVvLkpEm9HC+afOauUV1HHgpWheNqb62Y1R5OKeeefaG7s66xLQ98Yks3PB5J5NKC7XvHUhPwqnl4ZSh8XK757VD9DrlKh5PHFQZTXNvD3/NHNiswuP5GuS/v+5H7f89pnk2WZL3cl8NvBFO4e0o1vHxkmkh+uU9ZmxjhbmZJVVtvuY0VwEi7JP2E5BHjbX3LqeFR2BWuCMpk1pqdeC4dtUXnsiS3gf3f21xVybVBpeGFtGNZmxnz36DBd8kBiQRUvrg1niKcdPz42XBeYlGoNz64OY09sAe/ePVC3DFWpUDJj0Sliciv5ccpw7h7iTmxuJVP/PIm1mbZXlJejBXF5lTyxOAgna1NWzx3VYk+lS1Vdr2LJsXTG9XGiv9vZsj0fbInRy1qsbVDpkjjWzA3E1tyYuLxKnloZQm8XK36Zqv2+jycX8+amSMb0cuSjJvtMeRV1zFoShIWpIYtnjtBbVlMo1cxZpk23XzJrRLPeU2uDMnn3X23gb/q+t0Wl1vB/m6P5OziLaYFefHjvpdVYFK5+9hYmVNQp232cuH0RLlpSQRVJhdXc0+RO/2LIssxHW2NxsDDhxdvOnmmqVCj5cEssgz1sePKGs0tdvxxIJj6/is8f8MXZWrvMVdeg5ulVoViaGrJwhr9us1WWZd7YEMme2AI+vHeQLjDV1KuYuTiImNxKfpvuz91D3EkpqubxxaewNDVi7TxtYMopr2PmkiAsTYwuW2AC+PVAMsXV9bx029lly3/CclgXnM0zN/VmRA8H3SHnmNwKfpwyjD6u1mfHZ2rE4pkjsDbTBqv5K0Lo6WTJb9POpmhX1Cp5YnEQlQoVfz0xQi9VXanW8MyqUE6nl/LNI0P1EksA1gVn8dbmKG7qpz3f1VaDwUYKpZqnVoXyd3AWz03ofcnFf4Vrg1qWMTiPGbcITsJF2xdfCMDtAy+tDtqWyDyC0kt55fZ+ekkQP+5Noqi6nk/v99Vd1NKKa/jtYAr3DnPXS4D4dk8CyYXVfPfoML29r18PprApLIeXb+vL42eqiivVGp5eFUrEmTM/tw10paiqnicWBwGwck4g3R0sqGtQM/dM/6ils0e02XPpUsTkVrDwcCoP+Hno0rHj8yt5a1MUI3rY8+KtfZBlbbPD3bEFvH3XQCb0d6W0poEZf52itkFbA9Ddzpys0lqeWKwNVktnjdSl29c1qHly2WnSimv4Y4a/3qxIrZF56e9w9sUX8tG9g/UOL4N26faNjZHc0NuJ36f7n1c5pvLaBqYtOsXeuAI+uGcQr9zeT1R+EJBlmfwKBS7W7bfQEMt6wkU7lVpCbxerS5pN1Dao+GRbLL4etnrFRNOKa1h6PJ1HA7ozpElfoU+2xWFsKPF/d56tVBCdU8FfR9OYMtJL747/aFIxX+9O4J6h7jw34WxFhHf/jeFQYhGfP+DLJN9u1KvUzF0eTHF1PX/PG60ruvref9HE5Vfy1xMBekttHamuQc3Lf0dgb2nCu2fOGZXVNDB/RQjWZkb8MtUPI0MD/jycytLj6cwe25PZN/Skul7FrCVB5JTVseLJQPq5WVNcXc/ji4OoV2lYv2C0bmbUoNLw9KoQQs6cRxrbpEGgRiPz1qZItkbm8dak/kwf5a03vo0h2by2IYIbejvx5+MB55XEkF2mDZBZpXV63YQFIbusjup6Fb3O48iJCE7CRUssqCagx8W1W2/0+6FUCirr+XWan96Szxc74jExMuCV289mg4VklLE3roDX7uiHS5PZ0Wc74rCzMNGrR1dRq+TV9RH4OFnq9RT6+3Qma4K0KdCPjdT2I/pwSyzhWeX8Pt1P15DvQEIh64Kzefom/dT1jiTLMu/8G01iYRVLZ43EzsKEBpWGBStDyKtQsGbuKFxszPgnLIdPtsdxp68bb981gHqVmvkrgonOreT36f6M7OlAlULJzCVB5FXUsWpOIH3PpJZrNDKvro/gQEIRn97vqxcoZFnmgy0xrAvO5vlb+jD/nKy9jSHZvLohgrG9zj8wRedUMHvpaRRKNSueHNliHT7h+nUkqRiAwJ7t/16IZT3hoijVGnIr6vC+gHI158ouq+WPQ9oMrqZpzhFZ5eyMyWf+jb10e0oAP+5LwsnKhFlje+g+FpRWyrHkEp65ubfeIdIvd8VTVF3Pd48O0+0/pRRV8+6/2gy/V88EvZ3R+dp09Bt9mDhYe+GuV6l5559o+rhY8cI5df060pJj6WwIyea5m3szvq8zGo22F9KpNG19Qn9vew7EF/Lqem0dwe8eHYZGlnludRjHkkv48sEh3DbQVZfIEJ9XxW/T/XXvpSzLvL8lhv8icnl9Yj+mBp5tDijLMl/sTGDZiQzmjuvJS+d8nxcTmI4mFfPoHycwMpBYv2CMCExCM/+E5+DjZElf1/ZnTiI4CRclv0KBLGtrwV2sz3fEI0nwvzv1i4n+sC8JewtjnmzS0iEhv4pDiUXMHNND79zNH4dScLA0YerIsxfexIIq1gRlMmOUt25JsHEGYW5iyLePDsXQQKKspoG3/4likLsNrzY5r7PiRAbZZXW8N3nQebe7uFC7YvJ1Pa9evFWbBPHFznj+Cc/ltTv6ce8wD06nl/LUqhD6d7Pmz8cDMDYw4I2NUeyOLeC9yQN50N8TlVrDc2vCCDqTyND0UPB3e5NYfiKD+Tf66MoYNfp5fzK/H0phWqAX/7tzgN5+0OYwbWAa08uRPx8POK/SQv+E5TBzSRCe9hZsenqsXsalIIB2bzUorZSHAjzPa/9RBCfhoqSX1ABcUKHPpoLTS9kamce8cT56WWPRORXsjy9kzjgfvSKjq09lYGJkwLTAs3si+RUK9icUMnWkl94F9JcDyZgbG+q1IF8fkkVYZjnv3j0QF2vtkuB3exMpq1Xy9cNnz0+pNTKLj6Yxyseh3UOCF+tQYhHPrQ5jqKcd3z86HAMDiV8PJvPH4VRmjPLm6Zt6EZldzuwlp3G3M2fZrJFYmRrx4dZYNoZm8/JtfZk1tqeu/NCe2ALenzxIL5Fh+Yl0ftyXxMP+ns3abyw9lsY3exJ5YLgHH51TC3FLRC6vrItgVE9HFj1+fjXv/jycyot/hzOihwPrnxp92TIahavbT/uSsTI10vs/3BYRnISLEpNbCUA/1wu/Q9ZotMtN3WzNWHBOyaM/DqdibWrEjNFnf4EbVBo2h+UwabCbXvmdLRG5yDJ6VdBzy+vYEpHLtFHeuscqlGq+2pWIv7c99w/XXsDTimtYdSqTaYFeDOh2NtnhSFIRuRUKnjiT2dfRDiQUMm95ML1crFg2ayTmJoYsPprGlzsTuHeYOx/cM4iEgioeXxyErYUxq+YE4mhlyte7E1h6PJ2543rqkju+2JnA+hDtflHTQ7Y7ovJ4778Ybh3gymcP+DabFb2/JZbbB7ry5UND9A7g7o7J58W/wwnwduCvme3PmGRZ5qtd8br9sCWzRuhlWwpCo6C0UnbGaGtDnlvDsTUiOAkX5WBCIf3drHG0aj8l9Fybw3KIztG2qGi6RFdQqWBHVB6Pjuiud5E7mlxEpUKlVzkBYE9sAQO62egdAF4XnIVGhhlNss7WBmVSXF3P63ecTWf+9UAyxoYSz03Q32vZHVuApYkhEwZ0bM080HaanbssmN4uVqx8UpvmveRYGh+eqQH49cNDSSmqZvqiU5gZGbJ6zii62Zrz28EUfjmQwpSR3XVLcH8dTeP3QylMH+Wlt18UlFbKC3+H4+dlz89Tzx5EBm1gfG19JKN9HPlxiv7njiYV8+zqMAZ72LJ41ohmJYvOJcsy7/8Xc2ZcXvw0xU+UIxJapFCqeXNTJO62ZhfUIVkEJ+GCZZbUciqtlNsHXngWW22Diq92JTDE01avfh7AutNZqDSy3qwJYG9cIVamRtzQ+2yaeE29ipDMMm7ud/ZjsizzT1gOY3o56pYb1RqZxcfSCfC2123QF1fX8094Do8GdNdLuADtcuPIng4dutckyzI/7kvi+TVh+HnZs2beKBytTPn1YDIfbNF28/1xynDSi2uY8udJJEli9dxAvBwt9OoEfnyfdha0NTJX1wW4aamnlKJq5i4PxtPenEXnJDFEZJXz9MpQ+rlZs/Bx/bp24VnlzFsRjI+zJctmjWjWs6ml7+edf6N1yRSf3i8O1wqt+3pXAqlFNXz50NALao0igpNwwZafSMdAkph6nmvHTf12MIX8SgXvTR6ot6Sk0cj8HZzF2N6OeDvql0I6mlTMKB9HvXpsoZllqDUyo5pkhCUUVJFeUstdTfoVHU4qIrO0lplNMvw2nqkHeG4Q1Ghk0otrdRW+O0KlQslTK0P59swez4o5I7EyMeKzHXG6pbyfpg4npahaF5jWzB2Fj7MV64KzdOWCvnlEm8RxKrWEl/+OYEQPe75/7GwJodKaBmYvPY2RgcTSmSP1lj8zS2qZvfQ0jlYmLJk1QteaHiC1qJpZS4JwsjJl+WxtOntbtNU84lh5Ultw99xkCkFoKiitlL+OpTF9lNcF7+F2anCSJGmiJEkJkiQlS5L0ZmeORTg/OeV1LD+ZwX3DPC5447ugUsGfR1KZPNRdL3UctMEmu6yOB/30u+gWVinILK1llI/+4yOzKwD9rrD7z1SsuG3A2RndhpBsHCxN9KpYbA7LYbiXna5WXyOFSk2DWoN9Oxfo8xWSUcZdPx5hT5y2Wd83jwxFQtK15pg+yotvHxlGQn4VUxaexNBAYu28UfR2seK/iFze2BjJuD5O/DRFWy4oubCaeStC8HQw10vvbno2auHjAXrV0itqlcxcGoRallk2e6QuGQS0jQqfWBKEgSSxfPZIvbNjrfn1YAqLj6Uxa2wP3pzYXwQmoVU19SpeXR9Bd3sL3po0oP0vOEenHcKVJMkQ+AW4DcgGTkuS9J8sy7GdNSahfR9uiUECXm7SuuJ8fbEzHo0GXru9eZuFbVF5mBgZcPsg/VJIkVlngtCZw7GNYnIr8Ha00NubOpRQxIBuNrqLbJVCyd7YAh4b0V0360ourCI+v4r3Jzfv+qrWaLvQXuoKVU29iu/2JLL4WBruduasmz8af297beWHlSEEpZXy6u19eebm3pxOL+PJpaexMTdm9dxAvB0t2Rmdx0tnst8WztAGoaKqemYtDcLY0IBls87OcGRZ5r3/oglKK+WHx4bh7332ULRSrW0jn1Vay8onA3WVL0C7DzB3eTBFVfWsnTf6vAr3/heRy1e7ErhvmDvv3DVQBCahTZ/viCerrJa1c0ddVN+uzqwQMRJIlmU5FUCSpLXAvYAITl3UvrgCdsVoKzRcaJ256JwKNoXmMH+8T4t9kPbHFzK2l2Oz/Y74fG1WYP9u+uWD4vOqGNCkpFBtg4rQzDJmjz17NmpvXAH1Kg2Tm+xtbY/KR5JgUgsldaxMjTAxNKC0puGCvrdGao12z+urXQnkVyqYGujFW5P6Y21mTGxuJfNXBlNQWc8Pjw3j3mEe7I0t4JnVoXjYmbNyTiDudubsiyvguTVhDPW0ZfFMbSp300Dy97zReun7K09msCYoi6du6tWsJt4HW2I4nlLC1w8P1TsQq9HIvLI+gojscn6f7t8s8LckKruC19ZHMLKHA1+ck+UnCOc6mlTMipMZPHlDz4s+jN2ZwckDyGry72wgsJPGIrSjUqHk/zZH09fVirnjzj/jBhqrEcRjZ2HMMzf3bvb5zJJaMkpqmdUkHbpRUmE1HnbmekGrrkFNWkmNXtA5lVqKUi3rrWtvi8yjm62ZrpgqaAPW8O52LTZGlCSJXi5WhGWVX9D3p1Rr2B6Vx0/7k0kurGaIpy2/TBuOv7cDsiyf2TuKxs7chHXzRzOsux1rgzL5v3+iGeRuw5KZI3C0MuVAQiFPrQxlQDcblpw523RuIBnaJJCcTi/lgy2xTOjvoqt40WjlyQztvtCNPnqp9gA/7k9i25laencMar9ob0WdkqdXh+BoacJv0/0u28Fk4dpQqVDy+oYIfJwtee0CmlGeq8vX1pMkaR4wD8DLy6udRwuXywf/xVJYpeD3GWMvuFHc4aRijiQVN2u93uhYirbe1g3ntGkASC2qwcdZf8kpsaAKWYb+TaoQHE4qwtTIgBE9tHtTFXVKDicWM32Ut+4uv6BSQWR2RZv/YSYNduO7vYkcSSpq1jbiXEkFVWwOy2FjaDYFlfX0cbHi56nDuXNwNwwMtI0M3/0nmn/CcxnTy5EfHhuOo6UJX+6M59eDKYzv68yv0/ywNDXiQEIh85eH0MfVihWzA3VnQb7dk9hiIMmvUPDUylC6O1g06610KrWE9/+L4eZ+zrw+Uf8A7vaoPL7fm8SDfp7nldYryzL/2xRFXrmCdQtGX9TRAeHiXY3Xv8+2x5FfqWDjU2Mu6XhBZwanHKB7k397nvmYHlmWFwILAQICAuQrMzShqX/DtRfg52/pc15LQE1pNDJf7Iinu4O5rmXFuU6nl+JkZUKvc4KQLMukFdfwoJ/+clVUjnYfqmnbh4MJRYzu5aj7z7ArJp8GtUav19Se2AKANlPgn7yhJ9si83h2dRiv3t6XG/o442BpQr1KTWFlPSlF1YRnlXMkqZjkwmoMJBjf15nPHvDmpr4uukB4NKmYNzZGkl+p4KVb+/LshN5n+huFsCumgCkjvfjo3kEYGRqwP76ABStC6etmxconA3VtLjac6WT72IjueoGkscp4bYOK1XMD9Q415pbX8fSqULwcLPhhynC9oBWXV8kr6yLw87Lj0wcGn9ee0eawHLZF5fHaHf30ZqDClXG1Xf9CMkpZE5TF3HE9GX6Jvy+dGZxOA30kSeqJNig9BkztxPEILcgsqeX/Nkfj723P8xOaL8m1Z2dMPrF5lXz36NBWZ1zhWeUM627f7GJZWFVPdb0KH2erZo+3tzDG80xdv+TCKtKKa/QKwm4Kzcbb0YKhnmcD2PaoPHycLOndRrl+S1MjFj7uz/wVIbzzb0yLjzE3NsTP244Zo7yZNNhNL8utrKaBz3fE83dwFj5OlmxYMJrhXvakFdewYEUISYVVvHP3QGaP7YEkSeyMzue5NaH0d7NhxZNnEx1OpZbw1qZIxvbW72QL8OHWGEIzy/llqp+u+jhokxzmrwihXqVh4eP+erPU0poG5i4Pxsbc6Lx7MhVX1/Ph1lj8ve1ZcE7FckE4V2PXY3dbM129yEvRacFJlmWVJEnPArsAQ2CxLMstXw2ETqFUa3h+bRiSRLOKAudDpdbw9a4E+rhYMXlIy91yq+tVpBXXcN85m/kASQXVAM2CSXB6Kf7eDroL9vaofOBs08Os0lpOppby8m19dY8prFJwMrWEZ2/u3e6MwdvRkh0vjCM+v4rY3EpKaxowMzbA0coUH2dLejlbNesEq9bIbAjJ4sudCZTXKZk/3oeXbu2LmbEhO6PzeW19BEaGEstmj9QtF/4TlsMr6yPw9bBl2eyRuhlQapE2Zby7gwW/TvXXe631wVm6vaSm57lkWeZ/m6OIyqlg0eMBemny2jb1oRRW1bNu/ujzShkH+HhrLDX1Kr540FccshXatepUJvH5Vfw+3f+isvPO1eYzSJLkBiDLcr4kSc7AOCCho4KILMvbge0d8VxCx/tuTyLhWdo79IvpArslMpfU4hp+n+7XamCLz6tElmGQe/NmfrF52uW7prXvMktqSS+p1dWSa6wKEdjTQXfuau3pTAwkeDjgbCLAjqh8NDJ6SRRtkSSJAd1s9F67NceSi/l0exwxuZUEeNvz0X2DGdDNBoVSzfv/xbD0eDpDPW35ZZofnvbaTLvlJ9J5778YRvs4svDxAF3Cx7mHaRuX+EBb5eH//olmbG/HZvtmfx1NY1NoDi/d2levQzBoGzQeTynhm4eHnvey7ImUEv4Jz+X5Cb2bnQcThHOp1BoWHk5lZA8H7hjUMf3PWg1OkiTNB97U/lX6ApgJRAOfSZL0pSzLf3XICIQuKSitlN8OpfBIgKfeHfr50mhkfjuYQn836zbbuMfmaVPFWwoCEVkVeNiZ49Ck2sGeOO2+0U1nWkOcTi8jtbhGt+ykUKpZG5TFhP4udLM9G1D/i8iln6t1h1Z/iMwu5+vdiRxOLMLDzpwfHhvGPUPdkSSJ+PxKXvo7gri8SmaO6cFbd/bH1MgQWZb5bk8iP+5P5tYBrvw8dbhun6yxlXpehUJXvqhRYaWC+StCcLYy5acp+sH+UGIRn26PY+IgN72Ov6CdaS09ns6TN/TkwXOy9lpTr1Lz9j9ReNqb83QL2ZWCcK7t0fnklNfx/j2DOuz8W1szp2eBQYA5kAH0PjODsgcOACI4XaMqFUpe+jscLwcL3ps86KKeY3dsPokF1Xz/6LA2z8TE5VViZ2FMt3OqTciyTHBGabOOmdsic+nvZk3PM4dGV53KwNrUiLuHagPof+G5lNQ06J13yiipISSjjNcnXnxaa1PRORV8vzeRvXGF2FsY8787+/P46B6YGRuiVGtYeDiF7/cmYmtuzOKZAbpOug0qDW9ujGRTWA6PBHjy6f2+uiCjUmt4YW0Y4Vnl/DbNT6+ChkKpZv7KECoVSjYsGKMXrJMLq3l2dSj93Gz45pGheu91WGaZbqb11jltM9ry+8FUUopqWDprhCjmKrRLlmX+PJyKj5Mlt/TvuILJbQUnlSzLtUCtJEkpsiznnxlImSRJXT5rRLh47/0bQ36lgvULRl/02vGfR9LwcrBodxktIqsCXw/bZndbKUXVFFTW69XOSy+uITSznDfOpEcXVCrYHpXHtEBvLEy0Z4L+PJLKgG42jO519us2huYgSbS4r3UhwjLL+Hl/MvviC7ExM+KV2/oyc2wPXa26yOxy3toURUxuJXf5duOj+wbrAklJdT1PrQolKE27F/bchLN7X7Is8/Y/0eyOLeD9yQN1HXkbP/f6hkjCMrVt5Ac2Wf5sXAI0NTLgz8f11/nzKxTMWxGCq40pP09pfVn1XMmF1fxyIJnJQ911s1NBaMuptFKicir45P7BHXo4u60rj0aSJGNZlpXAXY0flCTJDFEw9pq1PSqPzWE5vHhrn4tOHY7JrSAko4x37x7Y5kZ6Tb2KhIIqnh7QPBOssU7ejX3PHqr9OzgLAwldT6alx9NRaWRdlt6BhEKSCqv57tGhesFua4T2nJH7ReybgTbV/af9yRxOLMLOwphXbuvLE2N76LLhKuqUfLcnkeUn0nG0MuX36X56ASYyu5ynVoZSXH22OkQjWZb5bEc8a09n8ezNvZnZZMYH2nNO/0Vou+M2fc7GqhH5lQrWzhul28sC7fLgnOWnqa1XsfLJsXpFYNuiUmt4dX0EZsYGvHP3hddCE65Pi46k4mBp0qwu5qVqKzi9CMgAsixnN/m4I7ChQ0chdAllNQ288080vh62PHsJew1rgjIxMTLgAb+2ZyrhWeWoNbJePbhG26PyGexho7vo1qvU/H06i1sGuOJma0aVQsnKkxlMGuyGt6Mlsizzy4FkPOzMubtJZmBqUTWpxTV6VcnPV1BaKd/tSeREagmOlia8Oak/M0Z562YoGo3MhtBsvtwZT0lNA9MCvXh9Yn9d0JJlmVWnMvlwSyxOViasXzBa1za+8fPf7Ulk4eFUHh/tzSvn1CtcG5TJT/uTeTSgO083acrY2HI+JKOMX6b66d1EaDQyL68LJya3kkWPB1xQu/RfDqQQnlXOT1OG6xWIFYTWpBRVszeukBdu6dPhS8BtBadlwO+SJH0jy7IaQJIkV+ALoD/wUYeOROh0n+2Io6JOyaq5gRecNt5IoVTzb3gud/t2a7f9wsnUEgwNpGbBKb24hvCss8t3oN1LKq1p0HWoXXUqkyqFSpcIEZJRRmhmOR/cM0gv9fpEaglAu9Uemjqdrg1Kx1NKcLIy5e27BjAt0FuvF01YZhkfbIklPKscPy87ls4aqXcouKJWyf82R7EtKo8b+zrz/aPD9PaKZFnmm92J/HwgmUcCPHl/sv5G8p7YAv63OYrxfZ35+H79c06f7Yhj65mqEecmq3yxK54d0fm8fdcAbhlw/llTR5KK+H5fIvcP9zjvjEZBWHQkDRMjg2btZzpCW8HJH/gcCJck6QXAF3gZ+BJ4vMNHInSq4PRS1gVnM3+8D/3d2k+fbs2hxCKqFCruG97+/s7xlBIGe9jq9RcCbVUCSULX+VaWtQ0D+7laM7a3Iwqlmr+OpnFDbyfdTOTPI6nYWRjrpY+DtmCpg6UJPVooNnuu0MwyvtuTyJGk4laDUkGlgi92xLMpLAdna1O+fngoDwz30FtrP5xYxGsbIiipbuCNif2Zf6NPs95VH2+LY/GxNKaM7M4n9/nqff54SjHPrA7F19OOX6f56QXbRUdS+fNIGk+M9m5WfmjVqQz+OJTKjFHePHmD/vJgWzJLanlhbTh9XKz45P7B5/11wvWtuLqeTaHZPOjnidNlKGvVanCSZbkMmH8mMO0FcoFR5yzxCdcAlVrD2/9oT3a/cEuf9r+gDfviCrA2M9JLSGhJpUJJeFY5T51TeUCjkdkYms3YXk66PaITKSXE5VXyxYPaTrCbQnMoqqrnh0eHAdpDt3tiC1gwvlez9uJltQ04WZm0mt6qzQos0+0pOVia8NYkbfZd06BUr1Kz6EgavxxIRqWWeeqmXjxzc2+9grRlNQ18tC2WTaE59HaxYtHjI/BtUqECtBl7b2yMZHNYDrPG9uCdu/SbLoZmljF3WTA9HC1YOnOEXpLDP2E5fLwtjkmD3Xj3nJnW/vgC3vknmgn9XXhv8vm3s6ioVTJraRAaWeaPGQHttmcXhEYrTmRQr9Jc0I3QhWjrnJMd2iW8QGAicCewQ5KkF2RZ3n9ZRiN0ig0h2cTnV/HrNL9LvjgdTynhht5OzSoonOtgQhFqjcz4fvrLbUeTi8kuq9MrWLroaBqOlibcO8wDjUZm0ZFUfD1sdQFwXbC2uH1LSwsOlqYcSSqmUqHUK+dTr1KzMzqfJcfSCc8qb3FPqdGB+EI+2BJDekkttw105e27Buh1620MqJ/tiKeyTsmzN/fm2Qm9m63Bl9c2MH9FCKea9HNqGkQis8t5YnEQTtamrHgyUC+RYX98Aa+uj2CUj0OzQq8RWeU8syqMge42/HQBlTwaz1VlldaxbPZIXXq+ILRHoVSz4mQGtw5wabMc2KVo60oUCvwKPCPLsgrYLUnSMOBXSZIyZFmecllGJFxRCqWa7/YmMtzLjkmD22+f0JaCSgXZZXXMbKH1xbl2RefjZGXaLCNwTVAm9hbGulPmacU17I8/u+F6IKGQ1OIafnhsGJIkIcsym8NyGNvbSe/QbaOpI71YE5TJk0tPM6G/K/UqNfF5VRxPKaZSoaKHowUf3juIh/w9mwXm7LJaPtgSy57YAnycLVk2eyTj++oH08jsct77L4awzHL8ve359H7fFpMQYnIrWLAyhIKKer5/dFizZc/wrHIe/+sUtubGrJ47Sq+lR1Baqa6VRtMOuKDdn2tswb74nJlWW+pVahasDCEks4yfp/i1O9MVhKb+Pp1FaU0Dcy6wfc6FaOs3+cZzl/BkWQ4HxkiSNPeyjUi4oladyqSgsp7vHx1+ySe7G1unD2/SOr0lCqWagwmF3DPMQ28GUFJdz964Ah4f3UNXmHTVyQyMDCSmBWrbBaw5lYmjpQmTzqRVJxdWk11Wx9M3tZxd6Otpy+sT+7E2KIsvdsYD4GFnzh2D3Jg81J0bejs1O5uhVGv462gaP+xNAuCNif158oaeeoVrCysVfLkrgQ0h2ThZmfDVQ0N40M+z2XPJssza01m8/18M9hYmrJ0/qllADk4vZdaS09hbmrB6bqBeqajI7HJmLz2Np705S2eN0NufK67WtlmXQdtm/Twz7OpVap5eGcqhxCI+f8D3oiqACNcvhVLNrweTGdnTgcCeDu1/wUVqa8+p1b0lWZb/vDzDEa6kBpW2msEoH4cOuXNOLKgCoF87CRXHU4qpaVA3q8H1X0QuSrXMIwHddePbGJrN7YNccbExo7y2gf3xhcwa20MXKILSSwG4obcTrXn6pt48fVNvKmqVmJsYttmP6nR6Kf+3OYrEgmpuG+jKe5MH6p0hakzG+PVAMg1qDfNv9OHZCb2bJXUAFFXV87/NUeyJLWBcHye+e3RYs43jw4lFzF8RgputGavnBurN/hLyq3h8cRB2FsasnBOo10upul7FrCWnKahUsGbuqGaV21tT16CdMR1KLOKj+wbz2Miro0eQ0HWs7sAb2raI3c/r2H8RuRRU1vPFg0M65PnSimtwsTZt1mr9XHtiC7AybZ40sSEkm8EeNrplsQMJhZTVKnn4TLDaG1eISiOfc46pBjNjA137jLY0LaJ6rtKaBj7fEce64Gw87MxZ9HiAXgFVWZbZHpXPp9vjyCmv47aBrvzfnQPo0cI+TeP5p8+2x1HToOb/7hzAkzf0bDar+jc8h1fXR9DbxZrls0fibH02+KQUVTNt0SlMjQxYNUc/aDWoNDy1MoTYvEoWzvA/7745VQolc5cHcyqtlC8e9OXRESIwCRemtKaBnw8kM9rH8bIvBYvgdJ2SZZklx9Lo62rVbB/lYmWX1dLdoe2UbbVGZk9sAeP7Oev1FEotqiYmt5K37zpbmWBndD52FsaMOzMrOp5SjKOlCUOaZMA1qDSYGRtedNkUlVrD6qBMvtmdSE29ivk3+vD8LX309m7i8ir5YEsMJ1NL6e9mzeq5gYzp1fJMLSKrnA+2aPst+Xvb88WDvs2qesuyzO+HUvliZzyjfBz4Y0aAXsPAjJIapv55UnuId85oveQLWZZ5c1MkR5KK+fLBIed9lqnkzBJgfF4V3z+qX6VCEM7XJ9viqKxT8t49Ay/7a4ngdJ0KyyonJreSj+87v46o5yOvQoGvh22bjwnNLKO4uoGJg/STL3bFaKuN3+mr3f+QZZlDiUVM6Oeiyz6LzK5guJd+U0J7SxMq65SU1za0e+i3KVnWBskvdyWQXFjNaB9HPrx3kF7V8opaJd/uSWDFyQxszI356L7BTB3p1WJJppSiar7dnci2qDycrEz58qEhPNTCHlS9Ss3bm6NZH5LN5KHufP3wEL0gnVVay9Q/T9Gg0rBm3qhmmVBf7kpgU2gOL9/Wl0dGdOd8ZJTU8MTiIPIrFfz5RAA3i5p5wkU4nlzMxtBsnrm51yWdhTxfIjhdpzaEZGNmbKA76HqpZFmmoFLRZgt00KZlGxpIzVLIDyUWMqCbje5sU0pRDaU1DbrCr7Isk1tex43nVHq4Y5ArP+5LYl1wFvNubL9bq1ojsysmnz8OpRCRXYGPkyV/zPDn9oGuuqCn0cisP9M4sKy2gemjvHn5tr4tBr/kwip+OZDCv+E5mBkb8vyE3sy90afFPaj8CgULVoYQnlXO8xN68+KtffWCV1ZpLY8tPEl1vYpVcwKbXQCWHEvjt4MpTAv0atYaozXRORXMXBKESiOzak6gXrVzQThfpTUNvLYhkh6OFjw34dLOQp4vEZyuQwqlmq0RuUwc5NbiRfRiVNapUCg1einQLTmWXIyfl53emSOlWkNoZjkzRp09p5SQr02uGORx9gKtXcLTT2YY5G7LuD5OfLkzAbUG5t3o02xmI8sy0TmV7IjOY1NoDvmVCrwcLPjiQV8e9PPUOxcUkVXOu/9GE5FdQYC3PcvvHckg9+azwajsCn49mMzOmHzMjAx58oaezB/fq9WT8gcTCnl5XQQKpZrfpvkxyVc/Qy69WLuUV9OgZtWcQL1SSKDdH/xwayy3D3Tlw3vPb7Z7MKGQp1eFarMEZ4+8bOdRhGubWiPz/Jowiqrr2bBg9BVroyKC03XoYEIhlQoV93dgFeHcijqAFs8aNaqpVxGVU8Ez5xSVTcivokGl0evSmluufb7GTDlJkrAxN6astqHZ8/481Y+3NkXyxc54fj+Ugp+XHQ6WpijVGgoqFcTlVVKpUGEgwfi+zrx/zyBuG+iqF8QKKxV8tSuB9SHZOFub8u0jQ7l/uIdeEJBlmRMpJfx2KIUjScVYmxnx9E29mD22p14mXVN1DWq+2BnP0uPp9Hez5uepfs2CRFJBFdMWnUKlkVk9N7BZMDyUWMQr68IZ2cOBH6cMP6+W6etOZ/G/zVH0dbVmyawR7d40CEJrvt6dwNFk7R5n08LFl5sITtehrZF5OFiaMLYDs21yyrTBxN2u9YtgVE4FGplm53zSimsA6ON69qLdoNYA6M2UhnjaciKlBFmW9YKGrbkxv0z1Y3dsAfviCojMriAhvwpjIwMcLU24e6g7/l723NzfRa/4KmhTshcdSWXh4VSUraSGy7LMvrhCfj6QTHhWOc7WprwxsT/TRnnpzQDPdSy5mP9tjiKjpJaZY3rw5qT+ze46G6tCGBkasHbeKPqe06k3JKOU+SuC6eNizZ9PBLR71yrLMt/vTeKHfUmM6+PEr9P8Omx2LFx/1gZl8tvBFKaM9DrvPc6OIoLTdaZepeZAfCH3DHO/6MrjLckuqwXQOxN0rugc7SHdc+vNFVQqAOhmc3bW1XgRrqlX6xIG7hnqzsvrIlgfkq07C9VIkiTuGOTGHYPOr8pFdb2KlSczWHg4ldKaBiYNduPNSf2blSXaHZvPj/uSic2rxNPenI/vG8xD/p5tBoms0lo+2xHH9qh8ejhasHpOIGNaOIfVuOzmYGnCqjmBeq8NjftFp+lma87yJ0e2GQhBuzz65sYoNoZm85C/J5894NtuGSlBaM3WyFzeOlMZ/4N7Lq4j9qUQwek6cyKlhJoGNbcPvLRSRefKKqvD3NgQJ6vWM+ZSiqpxsDRpti9Tr9LOkkybzJJ8nLUX6oT8Kt15ivuGebD2dBYfbY2lp5MlI3pc+OZ+Zkktq05lsCYok0qFinF9nHjl9n56S4qaM0kTP+xLIj6/ip5Olnz98FDuHebe5sU+r6KO3w+msDooE0MDiZdu7cv88T4tBrK1QZm8/U90q8tucXmVTP/rFDZm2gO47VV9rlIoeXpVKEeSinnx1j68cEufy3pAUri2HYgv5MW14YzwduD36f5tHly/XERwus7sji3AwsSwww/QpRRV08PJss0LYkFlfYvLfuZnLt51DWrdhTzA2x5TIwN2ROfpxmpgIPHNw0OZtugUj/5xgnk39mLW2B5t7qfIskxacQ0HEorYGZ3H6fQyDA0kJg5yY+6NPs2C0u7YfL7fqw1KPk6WfPfoUO4Z6tHmPk9CfhWLj6axKSwbWYaHA7rzwi19cLNtPi6VWsNnO+L562gaN/Z15pepw5stu8XlVTL1z5OYGxs2K2fUkvwKBTOXBJFcWM2XDw654ssvwrVld0w+z64Jo383axbNDNCrzn8lieB0HdHunRQwvq9zh2fcJBVUt9jRtqmKOqXeYdNGjcElp7xOV4nb2syYiYPd2BiSzVM39dIlWnR3sGDb8zfwwZZYfj+UwsLDKQzoZoOvhy2uNmaYGBmgVGsoqW4gvaSGuLxKiqu1SRR9Xa147Y5+PODnoZe4odbIbI/K45cDyecdlBpUGnbH5rPyZAYnU0sxMzbgsRFezLvRp9WDyEVV9Ty3JpSTqaXMHNODt+8a0GxpNSKrnCeWBGFubMiauaOaLfWdKy6vkllLTlOlULJ45ghu7KAD1cL1aV1wFm9ujMTX046lM0e0u5R8OYngdB2Jya2koLL+gjqkno/aBhU55XVMGXlxd+yNFR9CMsr0Uqhfua0fO6PzeX1DpF41bmszY75+eCjP3Nybf8JyOJ1eyp7YAkpqzmby2VkY093egvF9XfD3tmdML8dmpYbqGtRsDM1m0ZFU0ktq6eVsyfePDmPyUPdWg1JyYRV/n85iU2gOJTUNeNqb8/rEfkwZ4aXX4uJcBxMKeXV9JFUKJd8+MpQHWsiUPJ5SzNxlwThYmbDqyVF4tdMg8UhSEU+tDMXK1Ij1C8Yw0P3yH4wUrl1/HErhsx3xjOvjxO/T/c+7wv3lIoLTdeRgQiFAh5UrapRapM22a6/4qLWZEcXV9c0+7mlvjo+zJVsjc3miSbsNrzPtLN7YGMXc5cH8PNVPb+bV08mSl27rq/u3Sq1BpZExMpDaTPZIyK9iXXAW64OzqFSoGOppy2/T/LhjkFuLZZDqGtRsjcxl7eksQjLKMDKQuGWAC1NGejGuj3ObS35VCiWfbo9nTVAmfV2tWDUnsMWWGv9F5PLqugi8HS1Y8WRgi0uCTW0IyebNjZH0drFiyawRbabwC0JbZFnm853x/HEolbuHdOPbR4Z1yh7TuURwuo4cSixisIeNXoHRjpBZqs3U827nTt/dzpzQjLJmqeCSJPFIQHc+3xFPeFa53j7QoyO8MJAk3twUxS3fHOS5CX14wM+jxfRoI0MDjFpYrdRoZGLzKjmYUMj2qHxi8yoxMpC4Y7AbT4zuwYge9i3ulcXnV7LqZCb/hOVQVa/Cx9mStyb15wE/z3bfQ1mW2RqZx0dbYymurmf+jT68dFvfZsupsizz68EUvtqVwMieDvw5I6DNArVNU8Vv6O3Er9P9OnXpRbi6qdQa/rc5inXB2Uwf5cUH9ww+r3N0V4IITteJijoloZnlLBjf8c3BdAdm7doOTsM87Vh9KpOUoupmxVCnj/Lmr6NpvP9fDBsWjNab+Twc0J0B3Wx4/78Y3vsvhs93xDO2tyN+3vb0crbC1cYMK1MjJElb/aKiVklOeR2pxdo9p7DMcirqlAD4ednx7t0DuXeYe4sHZxtUGnbG5LPiRDqn08swMTLgLt9uPDaiOyN7OpxXBtzp9FI+3xFPSEYZg9xtWPh4gF7AbVTXoObNTZH8G57LfcPc+fzBIW3uBdar1Ly5MYrNYTk85O/Jp/f7dok7XOHqpFCqeX5NGLtjC3j+lj68dGvXyvAUwek6cSRJ2xb9chT9LKqux8TIABvztn+dGrPu9sQWNgtOVqZGvH3XAF5YG84HW2L58N5Bev9RBnvYsn7BaCKyK1gfnMXR5GL2xhW2+XpGBhK9nK2YOMiNkT0dGNfXqdWGfHkVdaw+lcmaoCyKq+vxcrDgf3f252H/7m3uJTWSZZljySX8fiiFo8nFOFub8tkDvjwS0L3FO9GMkhoWrAwlPr+S1+7ox9M39WrzwlBSXc+ClSGcTi/jldv68uyE3l3qQiJcXSoVSuYu07ZPeX/yQGaO7dnZQ2pGBKfrxMGEImzNjVu8g79UlXUqbMyM271YdnewYLiXHRtCsph/o0+z/Z17h3kQm1vJH4dTMTcx5PU7+unNoCRJYlh3O933UKlQklZUQ0lNPVUKFQCmRobYmBvhYWdON1vzNmcWKrWGQ4lFrAnKYn98ATIwoZ8L00d7M76P83m14aioVfJfRA4rT2aSUFCFk5Up/3fnAKaN8mrW9r3Rlohc3toUhaGBxJKZI7ipnRuGhPwqnlx2msKqen6cMpx7hnZMsV7h+lRcXc8Ti4NIyK/ih8e6bvsUEZyuA7IsczixiBv6OHVoVYgmr8D5LlPPHNODF9aGszs2n4mDm7cHf2Nif6rrVSw8nEpUdgWf3D+41UQLGzNjhl5gsG0sAvtfRA7/hOdSVFWPk5Up88f3YupIr3b7UYE2O/FAfBFbI3PZF1dIg1rDYA8bvnxwCPcMc291aa5SoeT9f2PYFJaDn5cdP04Z3mZFDYBdMfm89Hc4lqZG/D1v1Hk3FhSEluRXKJi26CQ55XVdvn2KCE7XgYSCKgqr6hnf5/KcgTE3NqK6XtUs0aEld/l244e9SXy5M4Gb+7vo9TIC7UHbT+73ZainHR9sieG27w5zp283HvTzYJSP40WdzyqraeB0einHziwF5pTXYWQgcVM/Fx7y9+SWAS7tlvkpq2lgX3whu2PyOZxUhEKpwcnKlKmBXjzg54Gvh22b3/uBhELe2hhFUXU9L9zSh2cn9G7zNTUame/3JvLj/mSGdrfjj+n+7WbwCUJbskprmbboFCXV9SybNZJAn8vbyfZSieB0HTicWATAuL4td2+9VN0dzKltUFNUVY9LO9WvjQwNeGfyQGYtOc0fh1J5/paWe8M8MqI7Ewa48NvBFDaEZLMlIhdTIwMGe9jSz80aT3tzXKzNsDYzwtTIAANJQqXRUKVQUVbTQF6FgtTiGhLyq3TZhGbGBtzQ25kXbunD7YNc221OmFtex87ofHbG5BOcXopGBjcbMx4J6M7EwW4E9nRsN7OpsErBR1vj2BKRSx8XK/6Y4d/ubK9SoeSlteHsiy/kIX9PPr5v8BVrUyBcm9KLa5jy50lq6lWsmjvqsizvdzQRnK4D++IK6e9mfdnOwjRWGT+UWMTDAe0fxL25nwuTh7rz45nK2a0tVTlZmfLO3QN5fWI/jiYVczylhKjsCrZH5VFeq2zzNUyMDPB2sGCwhw1TRnrh52XHMC+7ZjO1c+VV1LEtMo+tkXmEZ5UD0M/Vmmdu7s1tA13bnSE1qlepWXY8nR/3JdOg0vDirX146qZe7b5+Qn4VT60MIbO0lg/vHcSMUd4i8UG4JIWVCmYsPoVCqWbtvNFXzWFtEZyucRW1SoIzyph/Y8enkDca4mmLp705a4Iyecjf87wuph/fO5jQjDLmrwhh8zNj26wfZ2pkyC0DXPUqW9TUqyiu1iZC1Ks0yLKMsaEBlqZG2FsY42Bpct4X9Yo6JTui8tgclkNQeimyDIPcbXh9Yj8mDnJr93BxUxqNzPboPL7cmUBmaS0393PmnbsHntdz/Buew5sbo7A0NWLVnMAuv+widH0VdUoeXxxESXUDa+aOumoCE4jgdM07mFiIWiN3eMmipiRJ4qmbevF/m6PZF1fIre20agewtTBmyawRPPjbcWb8dYpls0aeVzJCI0tTo0sqr1KvUnMwoYjNoTnsj9cmNfR0suTFW/pyzzB3ejq1XdPuXGqNzM7ofH7ary0a28/VmmWzR55XNQ6FUs0HW2JYE5RFgLc9v07za3d5VBDaU69SM3dZMClF1SyeOeKCk4c6mwhO17i9cYU4WZlc9jXmh/27s/RYOv/3TxQBPezb3c8B6OtqzeKZI3hy6Wnu/eUYXz005LIGUaVaw8nUErZF5rEjOp+KOiVOViZMDfTi/uEeDPE8vyW7pioVSjaFZLPsRAZpxTX4OFvyw2PDuHtI6/X5mkoqqOK5NWHE51fx1E29ePm2vqIHk9AhPtseT1B6KT9OGc64y5QMdTmJ4HQNq1epORhfyJ2+3S57SRITIwO+e3QY9/1yjPf+i+GHx4af19eN6OHAP8+MZcHKEJ5cFswoHwemBnpz6wCXVs8JXYiymgaOpRSzP76Q/fGFlNcqsTQx5LaBrtw33IMbel94er1aI3MqrYTNoTlsjcyjTqlmuJcdP08dzqTB5/9erwvO4t1/o7E0MWLJrBFdOq1XuLrsjM5n6fF0Zo/tedWeixPB6Rp2NKmYqnoVEwd3bGPB1gz2sOX/27vz+KjrO4/jr09OkgCBHFw5SCikGK7ILXhUBBevYoWtFbWttmW3qz22ttVK6x52H7XVXduHVXdpdbvd7dqKVl2xq4LiKqjIIUTuKxyBQC5CQgi55rt/zOCiGyBBZn6/mXk/Hw8eDyczZD6GzLzne/w+3ztnDOfny3Zw9ZjB3T6Vdlhub5Z84xL+/d29PPHWbr751PskJRij8jIZObAPxbkZ5PdPY2DfXmRnpJCZlkxGanCXnpnR0Rmgua2TumOtVB5pYVfNMbZUNbJ+fwPbDx8Dgke5zxg5gNmjB53TkSFHW9pZuTMYcsu3VlPX3EZGSiKfHTeEm6cWMja/X7e/V+OJdn70/EZeWH+Qi4Zl84svlGkaT86byiPH+f4zGxibn8k9V430upxzpnCKYUvKq8hMS2Z6F0eEh8sdlw/nlU2Hue+FjVw8PKfb60IpSQl85eJivjytiFW761ixs5Z1+46wbMth6ta0nfbvmYFz///rWRkpjMnL5LqxQ5g2PIdx+Zk9GiEdbjzBur1HWLP3CKv31LPxwFECDvr2SuIznx7AlaMGcsXIgT0+iO29inq+u3gDBxpa+M6sEu64fLhvGm1K9HPO8aPnN9IZcPzypvFR3XtR4RSjTrR3snTzYa4eMyiiv6DJiQn8+PrRzH38bZ5YUXHa65hOJzHBmDY8h2mnBGrjiXYONrRQ3dhKfXMbDcfbaG7r/MguvfSURLIyUsjrl0ZxTga5fVJ7tH5Ud6yVlbvqeHtnLe/srmNvXfDaqJSkBMoK+vGNGSOYPjyH8YX9zqnLRltHgH9aup1/eXMXBf3T+cOCqUw8h2PmRc7k9a3VLN9Www+vueCs54H5ncIpRr25vYZjrR1cMzby880ThvZn5gUDeGJFBbdNL+ryeIue6Nsrmb6Dkhl5HmcnT7YxenXzIZZvq2bjgUYgeObUlOJsbp06lPFD+zNqSN+zXpt0NluqGvnu4g1sOtjITZML+OE1pZ4f5Cax5+RxKoVZ6R85Fy1aefIKMbMHgeuANmAXcJtzrsGLWmLVf288RGZaMtM+5c21MnfOGMH1j67kD6v389VLwneNVU8459h2uIkXNxxkSXkVe+uOk5hgjC/sx12zSrikJJfRQ/qet/6D7Z0Bfv1WBQ8v3U7ftCT+5dYJ3V6HE+mpN7bV8MGBo/xs7tiY2PHp1ce3pcAPnHMdZvZT4AfA3R7VEnNOtHfy6qZDXDt2iGe/pGUF/ZgwtD///u5ebp9e3K0O3+FSe6yV59YdYPHa/Ww/fCw4dfipbO74TLDrQ3eOxOipDyqP8r1nNrD1UBN/NmogP7lhLFlheB6Rkx5dvpP8/ml8brw/u4z3lCfh5Jx79ZSb7wLzvKgjVr29q5bmtk6uGuPtp/Tbphdx53++z7Ith7kywiOG9s4Ar22p5tl1lSzfWk1HwDG+sB/3zxnFVWMGk9PFQYPnw7HWDh55bQe/XlFBdkaKRksSEbtqjrFm7xHuvXpkTIyawB9rTrcDfzjdnWa2AFgAUFhYGKmaotrrW6tJT0n88HA/r8weNYi8fmk8unwns0oHRqRHXEVtM0+v2c8zayupaWolt08qX7m4mHkT8hkxsM/Zv8E5cs7xYnkVP/nTFqqOnuDGiQXce80FZKbpCHU5d919//vjukoSDK736dlM5yJs4WRmy4CuPjIudM69EHrMQqAD+N3pvo9zbhGwCGDixIldbBqWj3t7Vx1TirM+8UL+J5WUmMC3Z47ge8+U868r93D7xeE5bbPpRDsvlVexeG0la/ceIcFgxsgB3DS5kMtKcsN0htX/WbOnnn/40xbe39fABYP78ujN4z9shivySXTn/c85x/PvH+TSktyYul4ubOHknJt5pvvN7MvAtcAVznV1pYqci6Mt7eyuaWbu+HyvSwFg3oR8Xtl0mPtf2kxqcgLzJxeelxFUc2sH/7O9hpc+qGLZ5sO0dgQYMaA3d88eyQ3j8xgYgRfphv0NPPL6DpZtqWZg31R+OncM8yZ0fSy7SLjsqjnGgYYW7pwx3OtSziuvduvNBr4PXOacO+5FDbFqS1VwS/Qon3QfNjN+Of9Cvvpva1j43EaeXlPJ58qGMPVT2RRlZ3S7U8OJ9k62H25i9Z4jrNhRw8pddbR1BMjKSOHGSQVcf2EeFxb0C/vUoXOOd3bX8fgbu3hrRy2Zacl8Z1YJX72k+Ly0WxLpqRU7agG4OIIX20eCV6+mXwKpwNLQm8m7zrm/9KiWmFJ5pAWAouyeddUOp17Jifz29sksXrufRW/u5m9f3PzhfZlpyfRPD7YjSk9JpFdyIqlJCYDRGQhwrLWDQ40nONhwgs5AcIBdnJPBLVOGMrN0AJOLssI+bQfBo0eeXVfJ71fvY/vhY2RlpHD37JHcMrXwE1/HJfJJrNhZx9Ds9B519Y8GXu3Wi63xp48cbQkewtcv3V9vmAkJxo2TCrlxUiF7apspP3CUippm6ppbOXK8nebWDlraOmk60UFdRwAHJCUYvVOTGF/Yn+vL0ikd3Jeywn5hOzTx4z6cOiyvYumWw7R1BBibn8nP5o7ls2VDdDqteK4j1Gn/s2XR2dz1TDQPEWOSQusd7Z3+XcYrysmgqIfnJUWCc47dtc2s2BFs8PrOrjraOgNkZ6Qwf3Ih8ybkMzov0+syRT60ofIox1o7Ym5KDxROMWdI6ETZffXN5PYJz7U8saIz4Nh8sJHVe+pZG2rwWt3UCkBRdjpfvGgoM0sHMqkoS5scxJdW7qzFDC6KwVOTFU4xZmx+8JP9yp11TBiqxqKncs6xo/oYb26v4e1ddayuqKeptQOAvH5pTB2WzdRh2Uwfns1QH63ZiZzOih21jMnLDEuXE68pnGLMwL69uGREDk+9t48Flw7TugjB02afWVvJkvIqDjQEN4wMy8ngurIhTCnOYnJxVsTWsUTOl+bWDtbtO8LXLvVH78rzTeEUg75+2aeY/+tV/OOr21h4TanX5XiiM+BYuvkQT67cw3sV9SQlGJeW5HLnjOFcVpL74fSnSLR6r6KejoCLyfUmUDjFpGnDc7h5SiG/equCgqx0vnhRkdclRYxzjpc3HuKhV7exq6aZ/P5p3HPVSOZNyA9bPz0RL6zYWUtqUgIThsZmNxKFU4y677pSDje2ct8Lm6hvbuObM0Z42hk8EtbtO8L9Szbz/r4Ghg/ozSM3XcjVYwZrM4PEpJU7a5lUlBWzU/cKpxiVmpTIYzeP555ny/n5sh2s2XOEB+aOIb9/bF2oB3CkuY2fvryV36/ez4A+qfxs7ljmTshXKEnMqm46wdZDTdw9O3YavX6cwimGpSQl8I+fH8fEoix+/NJmrnz4Te64fDi3Ty8mLSX6P22d7AT+t/+1icaWdhZcOoxvXjGC3jplVmLc2zvrgNhrWXQqvYpjnJkxf0ohl5bk8HcvbubBV7bxb2/v4UvTipg/uTBqt6BWHW1h4XMbeX1rNePyM/np16YwcpA/+gmKhNvKncG+jqU+6aEZDgqnOJHfP51ffXEiq/fU84tlO3jwlW38fNl2rhg5kOvGDeHykblR0bjUOcfiNZXcv2QzHQHHD6+5gNumF2sKT+LKqop6phTH9sXh/n83kvNqUlEW//HVKWw71MTTa/bzwvoDvLzpEOkpiVxZOpD5U4Yyqah/RA4G7KkDDS3c82w5b+2oZUpxFg/OG0dhduytoYmcycGGFvbVH+dL04q8LiWsFE5x6tOD+vCja0u59+oLWLW7jhfLq3ip/CDPrz/I6Ly+3D17JJeMyPW6TCB4zdJv39nDQ69swwF/P2cUt0wZGvO7D0W6sqoiuN40dVhsd4BROMW5xARj2vAcpg3P4b5rS3l+/QEeXb6TW594jxsuzOPvrx/t6QaDbYeauPvZctbvb+Cyklx+fP3omDsaQKQnVu2up2+vpJhfY1U4yYfSUhK5aXIhN4zP47Hlu3jk9R28v7+B39w2KeK95ppbO/jFazt4ckUFfXol8fMby5hTNsSX040ikbSqop7JMb7eBBD+U9ok6qQmJfLXs0p46mtTaTjextzH3/nwhN1w6ww4nllbyeUPvcGiN3czb0I+r931Ga6/ME/BJHGv9lgrFbXNTC6O7Sk9UDjJGUwZls3iv7yI5ETjC4veZeOBo2F7Luccb+2o4bpHVvDdxRsY3C+N5/5qGg/MHUtWlG53FznfNh0Mfkgck9fP20IiQOEkZzR8QB+e/ouL6J2axPxfvcvavfXn9fs753hjWzV//s/vcOsT73G0pZ1ffKGM574+jQsLY7NnmMi5OjmDUTo4ttebQGtO0g0FWen84S+mcsuvV3HTr1bx4LyxzCn7ZG1Tjrd18FJ5Fb95ew+bDjYyJLMX988ZxecnFZCaFP3dK0TCYfPBRvL6pZGZnux1KWGncJJuye+fzh//ajoLfruGb/1+PUs3H+Y7s0oYltu729+jvTPA6op6XiyvYsmGgzS1djBiQG9+csMY5o7PJyVJA3mRM9lc1cgFcTBqAoWT9EBWRgpPLZjKY8t38dgbO3npgyouGZHLrNKBjMnLZGhWOn3TkkkwaGnvpLoxuHi75VAj6/Y2sGp3HU2tHaSnJDJ71CA+P6mAKcVZ2ugg0k17apuZVTrQ6zIiQuEkPZKcmMC3Zo5g/pRCfvvOHl5Yf5AfPb/xrH9vWE4G144bzGUlA7isJDcmGs+KRFJ7Z4COgKMgBk8W6IrCSc5Jbp9U7rry03xnVgn76o+z7VATlUdaONrSDkCv5ERy+6QyNDudkgF94mKOXCSc2joCABRkxccpzgon+UTMjKHZGRG/SFck3nQEHBD8YBgPtAItIhIFAi4YTunJ8TGmUDiJiESB0MApbtZrFU4iIlEgEEqndIWTiIj4xclpvV7JCicREfEJ5yA1KSHmu5GfpHASEYkCAefiZkoPFE4iIlEh4CA9JT526oHCSUQkKgSci5udeqBwEhGJCoGApvVERMRnAi5+duqBwklEJCpoQ4SIiPiOwklERHwn4CAtTvrqgcJJRCQqaOQkIiK+o916IiLiOw7t1osYM7vLzJyZ5XhZh4hINNDIKQLMrAC4EtjnVQ0iItFE4RQZDwPfJzhaFRGRs0hTb73wMrM5wAHn3IZuPHaBma0xszU1NTURqE5ExB9Off+D+Bo5hS2GzWwZMKiLuxYC9xKc0jsr59wiYBHAxIkTNcoSkbhx6vtf6uARLi2ONkSELZycczO7+rqZjQGKgQ1mBpAPrDOzyc65Q+GqR0Qk2sVTV/KIT2A65z4ABpy8bWZ7gInOudpI1yIiEk3iaVpP1zmJiESJ1KT4CSfPt34454q8rkFEJBokmNcVRI5GTiIi4jsKJxER8R2Fk4iI+I7CSUREfEfhJCIivqNwEhER31E4iYiI7yicRETEdxROIiLiOwonERHxHYWTiIj4jsJJRER8R+EkIiK+o3ASERHfUTiJiIjvKJxERMR3FE4iIuI7CicREfEdhZOIiPiOwklERHxH4SQiIr6jcBIREd9ROImIiO8onEREokRCgnldQsQonEREokSiKZxERMRnEjVyEhERv9G0noiI+I6m9URExHcS4ugdO47+V0VEoptGTiIi4jvaECEiIr6jDREiIuI7mtYTERHf0bSeiIj4ToJGTiIi4jcaOYmIiO/EUTYpnEREooVpWk9ERPwkfmIpSOEkIiK+o3ASEYkC8TSlBx6Gk5l9w8y2mtkmM/uZV3WIiIj/JHnxpGZ2OTAHGOecazWzAV7UISIi/uTVyOnrwAPOuVYA51y1R3WIiIgPeRVOJcAlZrbKzP7HzCad7oFmtsDM1pjZmpqamgiWKCLirVPf/5xzXpcTUWGb1jOzZcCgLu5aGHreLGAqMAl42syGuS5++s65RcAigIkTJ8bXv46IxLVT3//S80ri6v0vbOHknJt5uvvM7OvAH0Nh9J6ZBYAcQEMjERHxbFrveeByADMrAVKAWo9qERHxvfjaSO7Rbj3gSeBJM9sItAFf6mpKT0RE4pMn4eScawNu8eK5RUTE/9QhQkQkClicTewpnEREokF8ZZPCSURE/EfhJCIivqNwEhGJAnE2q6dwEhGJBsNye3tdQkQpnEREokByYnyNnRROIiLiOwonERHxHYWTiIj4jsJJRER8x6Kp36qZ1QB7I/R0OfinU7qfagHVczaq58z8VI/XtdQ652Z354Fm9nJ3HxsLoiqcIil08uREr+sAf9UCqudsVM+Z+akeP9UiH6VpPRER8R2Fk4iI+I7C6fQWeV3AKfxUC6ies1E9Z+anevxUi5xCa04iIuI7GjmJiIjvKJxERMR3FE5nYWZ3mZkzsxyP67jfzMrNbL2ZvWpmQzyu50Ez2xqq6Tkz6+dxPX9uZpvMLGBmnmwNNrPZZrbNzHaa2T1e1PCxep40s2oz2+iDWgrMbLmZbQ79O33L43p6mdl7ZrYhVM/feVmP/H8KpzMwswLgSmCf17UADzrnxjrnyoAlwH0e17MUGO2cGwtsB37gcT0bgRuAN714cjNLBB4FrgJKgZvMrNSLWk7xG8AvF212AHc550qBqcAdHv98WoEZzrlxQBkw28ymeliPfIzC6cweBr4PeL5rxDnXeMrNDDyuyTn3qnOuI3TzXSDf43q2OOe2eVjCZGCnc263c64N+D0wx8N6cM69CdR7WcNJzrkq59y60H83AVuAPA/rcc65Y6GbyaE/nr/O5f8onE7DzOYAB5xzG7yu5SQz+wcz2w/cjPcjp1PdDvy310V4LA/Yf8rtSjx88/UzMysCLgRWeVxHopmtB6qBpc45T+uRj0ryugAvmdkyYFAXdy0E7iU4peeLepxzLzjnFgILzewHwJ3A33hZT+gxCwlO2fwunLV0tx7xNzPrDTwLfPtjswER55zrBMpC66XPmdlo55zn63MSFNfh5Jyb2dXXzWwMUAxsMDMITlmtM7PJzrlDka6nC78D/kSYw+ls9ZjZl4FrgStcBC6Y68HPxwsHgIJTbueHviYhZpZMMJh+55z7o9f1nOScazCz5QTX5xROPqFpvS445z5wzg1wzhU554oITtGMD2cwnY2ZjTjl5hxgq1e1QHBnGsH1uM865457WYtPrAZGmFmxmaUAXwD+y+OafMOCn/KeALY45/7JB/XkntxhamZpwCw8fk3JRymcoscDZrbRzMoJTjd6uhUX+CXQB1ga2t7+z14WY2afM7NK4CLgJTN7JZLPH9occifwCsHF/qedc5siWcPHmdlTwDvAp82s0sy+4mE504FbgRmh35f1Zna1h/UMBpaHXk+rCa45LfGwHvkYtS8SERHf0chJRER8R+EkIiK+o3ASERHfUTiJiIjvKJxERMR3FE4St0KdsivMLCt0u3/odpGZvWxmDWam7cUiHlA4Sdxyzu0HHgceCH3pAWCRc24P8CDB63JExAMKJ4l3DwNTzezbwMXAQwDOudeAJg/rEolrcd1bT8Q5125m3wNeBq50zrV7XZOIaOQkAsEDAquA0V4XIiJBCieJa2ZWRrDp51Tgr81ssLcViQgonCSOhTplP07wbKF9BDdBPORtVSICCieJb18D9jnnloZuPwZcYGaXmdlbwGLgilBH7z/zrEqROKSu5CIi4jsaOYmIiO8onERExHcUTiIi4jsKJxER8R2Fk4iI+I7CSUREfEfhJCIivvO/B0J0NPUENl8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='X1', y='X3', data=df, kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077650</td>\n",
       "      <td>0.682146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>-0.077650</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>0.682146</td>\n",
       "      <td>0.676039</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X1        X2        X3\n",
       "X1  1.000000 -0.077650  0.682146\n",
       "X2 -0.077650  1.000000  0.676039\n",
       "X3  0.682146  0.676039  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.983047</td>\n",
       "      <td>3.957817</td>\n",
       "      <td>2.961956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.128419</td>\n",
       "      <td>1.227702</td>\n",
       "      <td>1.742270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.618476</td>\n",
       "      <td>-2.095366</td>\n",
       "      <td>-0.429207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.980254</td>\n",
       "      <td>2.510929</td>\n",
       "      <td>0.275211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.039416</td>\n",
       "      <td>-1.816161</td>\n",
       "      <td>0.131335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2        X3\n",
       "0  0.983047  3.957817  2.961956\n",
       "1  1.128419  1.227702  1.742270\n",
       "2  0.618476 -2.095366 -0.429207\n",
       "3 -0.980254  2.510929  0.275211\n",
       "4  1.039416 -1.816161  0.131335"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1: Summe -0.018769206873067365\n",
      "X2: Summe 0.901541277018557\n",
      "X3: Summe 0.43200143163621113\n"
     ]
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    my_column = df[column]\n",
    "    print(f'{column}: Summe {np.mean(my_column)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X1   -0.018769\n",
       "X2    0.901541\n",
       "X3    0.432001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2.634273\n",
       "1      1.366130\n",
       "2     -0.635366\n",
       "3      0.601962\n",
       "4     -0.215137\n",
       "         ...   \n",
       "995   -0.898159\n",
       "996    1.895477\n",
       "997    1.560335\n",
       "998    0.681487\n",
       "999    1.657227\n",
       "Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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